{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4066f8d3",
   "metadata": {},
   "source": [
    "# Detecting Model Misspecification in Amortized Posterior Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2df8c31",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Detecting-Model-Misspecification-in-Amortized-Posterior-Inference\" data-toc-modified-id=\"Detecting-Model-Misspecification-in-Amortized-Posterior-Inference-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Detecting Model Misspecification in Amortized Posterior Inference</a></span><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Introduction</a></span></li><li><span><a href=\"#Model-specification\" data-toc-modified-id=\"Model-specification-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Model specification</a></span></li><li><span><a href=\"#Training\" data-toc-modified-id=\"Training-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Training</a></span><ul class=\"toc-item\"><li><span><a href=\"#Training-loop\" data-toc-modified-id=\"Training-loop-1.3.1\"><span class=\"toc-item-num\">1.3.1&nbsp;&nbsp;</span>Training loop</a></span></li><li><span><a href=\"#Diagnostics\" data-toc-modified-id=\"Diagnostics-1.3.2\"><span class=\"toc-item-num\">1.3.2&nbsp;&nbsp;</span>Diagnostics</a></span></li><li><span><a href=\"#Inspecting-the-summary-space\" data-toc-modified-id=\"Inspecting-the-summary-space-1.3.3\"><span class=\"toc-item-num\">1.3.3&nbsp;&nbsp;</span>Inspecting the summary space</a></span></li></ul></li><li><span><a href=\"#Observed-Data:-Misspecification-Detection\" data-toc-modified-id=\"Observed-Data:-Misspecification-Detection-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Observed Data: Misspecification Detection</a></span><ul class=\"toc-item\"><li><span><a href=\"#Visualization-in-data-space\" data-toc-modified-id=\"Visualization-in-data-space-1.4.1\"><span class=\"toc-item-num\">1.4.1&nbsp;&nbsp;</span>Visualization in data space</a></span></li><li><span><a href=\"#Detecting-misspecification-in-summary-space\" data-toc-modified-id=\"Detecting-misspecification-in-summary-space-1.4.2\"><span class=\"toc-item-num\">1.4.2&nbsp;&nbsp;</span>Detecting misspecification in summary space</a></span></li></ul></li><li><span><a href=\"#Hypothesis-test-for-observed-data\" data-toc-modified-id=\"Hypothesis-test-for-observed-data-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Hypothesis test for observed data</a></span></li><li><span><a href=\"#Sensitivity-to-Misspecification\" data-toc-modified-id=\"Sensitivity-to-Misspecification-1.6\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>Sensitivity to Misspecification</a></span><ul class=\"toc-item\"><li><span><a href=\"#Computing-Sensitivity\" data-toc-modified-id=\"Computing-Sensitivity-1.6.1\"><span class=\"toc-item-num\">1.6.1&nbsp;&nbsp;</span>Computing Sensitivity</a></span></li><li><span><a href=\"#Plotting-the-results\" data-toc-modified-id=\"Plotting-the-results-1.6.2\"><span class=\"toc-item-num\">1.6.2&nbsp;&nbsp;</span>Plotting the results</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39f53e2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "import bayesflow as bf\n",
    "from bayesflow import computational_utilities as utils\n",
    "\n",
    "matplotlib.rcParams[\"figure.dpi\"] = 72"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e175999-940f-4220-8696-7ba877600f89",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c34cc2c7-65d4-43b5-b71b-0515443a1abb",
   "metadata": {},
   "source": [
    "Under certain regularity conditions, the theory on simulation-based inference assures that the (amortized) neural posterior estimator $q_{\\phi}$ samples from the exact posterior $p(\\theta\\,|\\,x)$ after convergence.\n",
    "\n",
    "However, the neural posterior approximator is optimized with respect to the \"prior prredictive distribution\" of the generative model which we specify for the training process.\n",
    "When the generative model at test time (aka \"true data generating process\") deviates from the one used during training, the guarantees for the approximate neural posterior no longer hold and the approximate posterior samples can be wrong in essentially arbitrary ways.\n",
    "\n",
    "The precise definition of model misspecification in amortized inference, along with extensive implications and experiments, can be gleaned in the following pre-print: https://arxiv.org/abs/2112.08866"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69c3be58",
   "metadata": {},
   "source": [
    "<img src=\"img/model_misspecification_amortized_sbi.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dab21025-c5ad-406b-a7cb-d5c59d129601",
   "metadata": {},
   "source": [
    "## Model specification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb30c6ac",
   "metadata": {},
   "source": [
    "The general Bayesian forward model can be formulated as a two-step process:\n",
    "\n",
    "$$\n",
    "\\theta \\sim p(\\theta) \\qquad x\\sim p(x|\\theta)\n",
    "$$\n",
    "\n",
    "\n",
    "For this showcase example, we specify a fairly simple generative model where the means of a 2-dimensional Gaussian shall be estimated: $\\theta=\\mu=(\\mu_1, \\mu_2)$. The likelihood $p(x|\\theta)$ is a Gaussian $\\mathcal{N}(\\mu, \\Sigma)$ with location $\\mu$ and covariance matrix $\\Sigma$. The prior distribution $p(\\theta)$ over the inference targets is again a Gaussian $\\mathcal{N}(\\mu_0, \\Sigma_0)$ with location $\\mu_0$ and covariance $\\Sigma_0$.\n",
    "\n",
    "Consequently, the forward model is\n",
    "\n",
    "$$\n",
    "\\mu\\sim\\mathcal{N}(\\mu_0, \\Sigma_0) \\qquad x\\sim\\mathcal{N}(\\mu, \\Sigma)\n",
    "$$\n",
    "\n",
    "with fixed parameters $\\mu_0, \\Sigma_0, \\Sigma$, and we want to perform posterior inference over the parameter vector $\\mu$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de375a6",
   "metadata": {},
   "source": [
    "We choose $\\mu_0=0, \\Sigma_0=\\mathbb{I}, \\Sigma=\\mathbb{I}$ as fixed parameters of the generative model for training the neural posterior approximator. Each simulated data set contains $N=100$ observations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "35decbdc-2fcd-4afd-9c73-e96d7572ec53",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 2)\n",
      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 100, 2)\n",
      "INFO:root:No optional prior non-batchable context provided.\n",
      "INFO:root:No optional prior batchable context provided.\n",
      "INFO:root:No optional simulation non-batchable context provided.\n",
      "INFO:root:No optional simulation batchable context provided.\n"
     ]
    }
   ],
   "source": [
    "def prior(D=2, mu=0.0, sigma=1.0):\n",
    "    \"\"\"Gaussian prior random number generator.\"\"\"\n",
    "    return np.random.default_rng().normal(loc=mu, scale=sigma, size=D)\n",
    "\n",
    "\n",
    "def simulator(theta, n_obs=100, scale=1.0):\n",
    "    \"\"\"Gaussian likelihood random number generator.\"\"\"\n",
    "    return np.random.default_rng().normal(loc=theta, scale=scale, size=(n_obs, theta.shape[0]))\n",
    "\n",
    "\n",
    "generative_model = bf.simulation.GenerativeModel(\n",
    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b6c728",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf7f806c",
   "metadata": {},
   "source": [
    "We choose a `DeepSet` architecture [1] to learn 2 permutation-invariant summary statistics from the data, which are then passed to the posterior network and jointly optimized.\n",
    "The Inference network is a standard `InvertibleNetwork` with two coupling layers and the `AmortizedPosterior` combines the inference and summary networks. Since we desire model misspecification detection via a structured summary space [2], we select `summary_loss_fun=\"MMD\"` and the amortizer combines its losses correctly.\n",
    "Finally, the `trainer` wraps the generative model and the amortizer into a consistent object for training and subsequent sampling.\n",
    "\n",
    "[1] Zaheer et al. (2017): https://arxiv.org/abs/1703.06114\n",
    "\n",
    "[2] Schmitt et al. (2022): https://arxiv.org/abs/2112.08866"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "57eff441",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Performing a consistency check with provided components...\n",
      "INFO:root:Done.\n"
     ]
    }
   ],
   "source": [
    "summary_net = bf.networks.DeepSet(summary_dim=2)\n",
    "inference_net = bf.networks.InvertibleNetwork(num_params=2, num_coupling_layers=2)\n",
    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer, memory=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3674126e",
   "metadata": {},
   "source": [
    "### Training loop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65269680",
   "metadata": {},
   "source": [
    "Because the inference problem is simple and illustrative, we just train for 15 epochs with 500 iterations per epoch and a batch size of 32."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48fc49d0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "losses = trainer.train_online(epochs=15, iterations_per_epoch=500, batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cbbdebb",
   "metadata": {},
   "source": [
    "### Diagnostics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "541347e4",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = bf.diagnostics.plot_losses(losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d1cde98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = trainer.diagnose_sbc_histograms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ffdb3cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_sims = trainer.configurator(trainer.generative_model(200))\n",
    "posterior_draws = amortizer.sample(new_sims, n_samples=500)\n",
    "fig = bf.diagnostics.plot_recovery(posterior_draws, new_sims[\"parameters\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00f3acc4",
   "metadata": {},
   "source": [
    "### Inspecting the summary space\n",
    "\n",
    "In fact, the summary space has essentially converged to a unit Gaussian for samples from the generative model which we used to train the networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8d02b65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simulations = trainer.configurator(trainer.generative_model(10000))\n",
    "summary_statistics = trainer.amortizer.summary_net(simulations[\"summary_conditions\"])\n",
    "theta = simulations[\"parameters\"]\n",
    "\n",
    "_ = bf.diagnostics.plot_latent_space_2d(summary_statistics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf961a09-c49f-4443-b1fb-584cdc80d7c8",
   "metadata": {},
   "source": [
    "## Observed Data: Misspecification Detection\n",
    "\n",
    "After assessing the converged neural posterior approximator's performance for the reference model used for training, we will now perform inference on data from a different data generating process. In a real-life analysis, this would be the observed data $x_{\\text{obs}}$ from an experiment or study."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a727c31",
   "metadata": {},
   "source": [
    "For this illustration, we choose the prior scale $\\tau_0$ as the source of misspecification. That means that we observe 1000 data sets $\\{x_{\\text{obs}}^{(k)}\\}_{k=1}^{1000}$ from a generative model with prior scale $\\tau_0=4$. Consequently, the prior covariance is $4\\cdot\\mathbb{I}=\\begin{pmatrix}4&0\\\\0&4\\end{pmatrix}$. The remaining fixed parameters $\\mu_0$ and $\\Sigma$ are unaltered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6633bfc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Performing 2 pilot runs with the Generative Model: Observed model...\n",
      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 2)\n",
      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 100, 2)\n",
      "INFO:root:No optional prior non-batchable context provided.\n",
      "INFO:root:No optional prior batchable context provided.\n",
      "INFO:root:No optional simulation non-batchable context provided.\n",
      "INFO:root:No optional simulation batchable context provided.\n"
     ]
    }
   ],
   "source": [
    "def prior_obs(D=2, mu=0.0, sigma=4.0):\n",
    "    \"\"\"Gaussian prior random number generator.\"\"\"\n",
    "    return np.random.default_rng().normal(loc=mu, scale=sigma, size=D)\n",
    "\n",
    "\n",
    "def simulator_obs(theta, n_obs=100, scale=1.0):\n",
    "    \"\"\"Gaussian likelihood random number generator\"\"\"\n",
    "    return np.random.default_rng().normal(loc=theta, scale=scale, size=(n_obs, theta.shape[0]))\n",
    "\n",
    "\n",
    "generative_model_obs = bf.simulation.GenerativeModel(\n",
    "    prior=prior_obs, simulator=simulator_obs, name=\"Generative Model: Observed\", simulator_is_batched=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "03dacb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
    "simulations = trainer.configurator(trainer.generative_model(1000))\n",
    "x = simulations[\"summary_conditions\"]\n",
    "\n",
    "# 1000 \"observed\" data sets with different prior covariance\n",
    "simulations_obs = trainer.configurator(generative_model_obs(1000))\n",
    "x_obs = simulations_obs[\"summary_conditions\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c11508c3",
   "metadata": {},
   "source": [
    "### Visualization in data space"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e16aca3b",
   "metadata": {},
   "source": [
    "Let's visualize some of the data $x_{\\text{obs}}$ from that generative model. This plot lives in the data domain $\\mathbb{R}^2$ and depicts the data $x_{\\text{obs}}$. Each color is one data set $k=1,\\ldots,1000$, and all points of one color form the respective data set $x_{\\text{obs}}^{(k)}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "23fdeb86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JJC+88MJer+3qq6/mxhtvLP0x0tfXx0MPPQTAhz/8YR555BGeffZZbNvmK1/5SsUvwf3hHe94B+FwmH//93/HcRyefPJJHn74YS655BJ0XeeDH/wgN910E5lMhjfeeKOi6fKCCy5gw4YN3H///TiOg+M4rFy5knXr1h2QtSkUij1nqp/lIo8++mjpPvLP//zPLF26lFmzZrFy5UpeeOEFHMchEokQDAbRNA1N0/jMZz7D5z//eXp7ewHo6Ojg97///ZRrueSSS/jDH/7Ad77znVL1GODjH/84Dz/8ML///e/xPI9cLseTTz7Jzp07aWtrY8mSJXz1q1/Ftm2effZZHn744UmvsXz5cn74wx/yxhtvkMlkxv1+TCaT1NbWEgwGefHFF3nggQdK+xoaGtA0bdzvz2g0Sjwep6Ojg9tvv33PXnjFWwYlkA8jLMviV7/6FT/84Q+pra3l5z//OR/84AdL+z/3uc+RzWapr69n6dKlnHfeeRWPv/766/nlL39JTU0N1113He9973s577zzWLhwIW1tbQSDwYq3oPaF//t//y+RSIR58+Zx1llncdlll/HXf/3X+3XOIpFIhMWLF7No0SIsywIKfzS0tbXR2NgIFET6I488wiuvvMLcuXOpr6/n05/+NCMjI3t9vZUrV/KOd7yDaDTKRRddxDe/+U3mzZtX2n/xxRdz6qmncvLJJ/O+972Pv/mbvwHgAx/4AF/+8pe55JJLiMViHH/88Tz22GMAVFVV8cc//pGHH36Y5uZmFixYwBNPPLHXa7v++uu56KKL+Mu//EuqqqpYunRpSWgvWrSIu+++m8suu4yWlhZqamqYOXPmXl9jIizL4uGHH+axxx6jvr6ea665hvvuu49jjjkGgG9/+9ukUimam5v55Cc/yac+9anSY6uqqvjDH/7Az372M1pbW2lububLX/4y+Xz+gKxNoVDsObv7WYZCVfXmm2+mtraWl19+mR//+McAJBIJPvOZz1BTU1NKy/nSl74EwNe//nXmz5/P0qVLicVi/MVf/AXr16+fci0tLS2cccYZPPfccxVpQbNmzeKhhx7i1ltvpaGhgVmzZnH77beX/uB/4IEHeOGFF6itreXmm2/miiuumPQaf/VXf8XnPvc5zjnnHObPn88555xTsf8///M/+cpXvkJVVRW33HILy5cvL+0Lh8PceOONvPOd76S6upoVK1bw1a9+lVWrVhGPx3nf+95X8btY8fZASPWegUIxDiEEGzduLL0VqFAoFAqF4u2DqiArFAqFQqFQKBRlHBCB/Nd//dc0NjZy/PHHl7YNDg5y7rnnsmDBAs4991yGhoYmfOyPfvQjFixYwIIFCyYdIqFQKBSKqVH3YYVCoThwHBCLxdNPP000GuWKK64oTdv5+7//e2pra7nhhhu47bbbGBoa4utf/3rF4wYHB1myZAkvvfQSQghOPfVUXn75ZWpqavZ3SQqFQvG2Qt2HFQqF4sBxQCrI73rXu6itra3Y9tBDD/GJT3wCgE984hOlMcHl/P73vy+Npa2pqeHcc8/ld7/73YFYkkKhULytUPdhhUKhOHBMmwe5p6eHlpYWoJA3O9Egh46OjopUhZkzZ1aEmCsUCoVi31H3YYVCodg3DkqTnhBiwiDuveHee+9lyZIlLFmypDQJTaFQKBR7hroPKxQKxZ4zbQK5qamJrq4uALq6uko5tuXMmDGjYvLNzp07J53KduWVV/LSSy/x0ksvlUYYKxQKhWJy1H1YoVAo9o1pE8gXXXRRqRv6Rz/6ERdffPG4Y9773vfyhz/8gaGhIYaGhvjDH/7Ae9/73ulakkKhULytUPdhhUKh2DcOiEC+9NJLOeOMM1i/fj0zZ87kv/7rv7jhhhv44x//yIIFC3j88ce54YYbAHjppZf49Kc/DUBtbS3//M//zGmnncZpp53GV77ylXFNJgqFQqHYPeo+rFAoFAeOI3KSXjGSSKFQKBSHBnUfVigUb2XUJD2FQqFQKBQKhaIMJZAVCoVCoVAoFIoylEBWKBQKhUKhUCjKUAJZoVAoFAqFQqEoQwlkhUKhUCgUCoWiDCWQFQqFQqFQKBSKMpRAVigUCoVCoVAoylACWaFQKBQKhUKhKEMJZIVCoVAoFAqFogwlkBUKhUKhUCgUijKMQ70AhUKhUCgUCsW+sWpgCw+2r6AzO0hrqJbls5eyuG7eoV7WEY+qICsUCoVCoVAcgawa2MJd6x9lyE5RZ1UxZKe4a/2jrBrYcqiXdsSjBLJCoVAoFArFEciD7SsI6RYRI4gQgogRJKRbPNi+4lAv7YhHCWSFQqFQKBSKI5DO7CBhPVCxLawH6MwOHqIVvXVQHmSFQqFQKBSKaWAgu5LtiZ+ScXcSNmbSFruUutBpB+z8raFahuwUESNY2pbx8rSGag/YNd6uqAqyQqFQKBQKxQFmILuSdYO3Y3sDBLR6bG+AdYO3M5BdecCusXz2UrKeTdrNIaUk7ebIejbLZy/do8evGtjCDasf4Irnvs0Nqx9Q3uUyVAVZoVAoFAqF4gCzPfFTDBHC0KIAGCIKfmH7gaoiL66bx+eOPr8ixeKqPUyxKDb4hXSLOquK9nQff7f6x9SYYRbEWt/2aRhKICsUCoVCoVAcYDLuTgJafcU2XUTIuDsP6HUW183bJyFb3uA3Yqfpyg0DkPHsUhrG544+/20rkpXFQqFQKBQKheIAEzZm4sl0xTZPpgkbMw/Riiopb/DrzA6jCw1T6OQ8R6VhoCrICoVCoVAoFAecttilrBu8HfxC5diTaVyZZUHs0v0+94EYDlLe4JfzbSxh4EqfoG4CKg1DVZAVCoVCoVAo9oCB7EpW9XyBZzuWs6rnC1M23NWFTuPY2i9h6XXk/X4svY5ja7+03/7jAzUcpLzBLyBMbOniS5+WUA2g0jBUBVmhUCgUCoViNxRTKQwRqkilmEr01oVOO6CxblDpHQZKH4t2iIkqy5NVnIsNfgNmCtt2aQ7FiZvhUhrGVXuYhvFWRAlkhUKhUCgUit1wMFIp9oTO7CB1VlXFtrAeYGOisyKVolhZPr/lZB7temXc9mIDXtGaMVZE72kaxlsVJZAVCoVCoVC85dnfoR0HK5Vid0w2HCTnu9RPUFm+f9uzzAzXTlhxLhfAxc+LIrlYkd6dSD4QfujDEeVBVigUCoVC8ZbmQAztOFxSKSYbDhLSzQnHTiec7B6No94Xb/OB8kMfjiiBrFAoFAqF4i1NuT1CCIGhRTFEiO2Jn+7xOdpil+LKLK6fQkqJ66dwZZa2A5BKsTcUvcM1VpQBO0mNFeVzR5/P/KoWMl6+4tiMlydmhibcPrYBr9zbLITYo6i3fXnMkYKyWCgUCoVCoXhLUrRV9GaexhARwtZMLK0a2Ht7RDGVotymsWAvbRoHismGg9y1/lGgUCHOeHmyns3lc87i0a5XKrb35xII4Irnvl2yRUzmbZ4q6m1fHnOkoASyQqFQKBSKtxzlqROGFsWXOVL2ZqLWUVha9T7ZI6YjleJAMdXY6YWx1tL2kGYhhEBChS0ipFlkvPw4b/NUUW+T+aH3Jh7ucPUwT6vFYv369Zx88smlf7FYjLvuuqvimCeffJJ4PF465pZbbpnOJSkUCsXbCnUfVhzp7E32cDnltoqQ0VranrF3HjJ7xHSzuG4et51yGfed+VluO+WyktAs314TiFIXqBpnixCCCb3Ny6eIepvMDz3VY8o5nD3M01pBPvroo3nllVcA8DyPGTNm8IEPfGDccWeffTaPPPLIdC5FoVAo3pao+7DiSGZfsoeLlKdOBPQaYB5ZtxPXT2HpdYfMHnEgmar6Otm+yWwRA3Zy0gr0ZExVtd4Tpsp0PtRV5INmsfjf//1fjjrqKNra2g7WJRUKhUJRhroPK4409iR7eLL4trAxE9sbKDyGgkjWhYml17G46c4Dus79jZDbF4rV14nyjYFJ901li5jM2zwV+/KYIoezh/mgpVj87Gc/49JLJ34r4/nnn+ekk07ir/7qr1i7du2Ex9x7770sWbKEJUuW0NfXN51LVSgUirck6j6sONLIuDvRRaRiW3lz3VTxbfuaOrG3lo4DESG3L0yVIDHVvn2xRawa2MINqx/giue+zQ2rHxhngdjd/sloDdXuUcLGoUBIKeV0X8S2bVpbW1m7di1NTU0V+xKJBJqmEY1GefTRR7n++uvZuHHjlOdbsmQJL7300nQuWaFQKN5SqPuw4khkVc8XClXg0QoyULJILG66c7f797ayW27p0EUET6ZxZXZKS8fu1jBdXPHct6mzqhBClLZJKRmwkwCT7rvvzM+W7BcbE53kfJeQbjK/qmXCBrnySnV5OkZxEt/u9k/F/jx2ujkoFovHHnuMxYsXj7spA8RisdLn559/Ptdccw39/f3U19ePO1ahUCgU+4a6DyuORNpil7Ju8HbwqRCsC0arwLubbre3qRMbhu4m63QicdFFkJA5AyldXun9MgGjfkKRfagm7O0uQWKqfUXxedf6R6kfFadjR1AXmcwnfM/Gx6lpj7JyYBO60Kg2I7S7/eQ8B1PTuWfj49xTd+W4dY/1Rp/fcjKvDLcfdiOuD4rF4qc//emkb+t1d3dTLGK/+OKL+L5PXV3dwViWQqFQvG1Q92HFkUgxe9jS68j7/Ugp0EWYdYO3s6rnC+iED9h0u4HsSkbyryPx0YSFj0PC3kDKaceVqUntE4dqwl65VWIon+L14XbeGOlg2E5xcvXs3dooHmxfget7tKcHeGV4G+3pAVzfGzfkY2Oik/Z0P6sHt7JuZCfDdhrbc3kj0cGQncKTPnnPZXumn6xnY2o6npS8keiY0IoxNrXi0a5XWD576bjkjUPNtFeQ0+k0f/zjH7nnnntK27773e8CcPXVV/PLX/6S73znOxiGQSgU4mc/+1nFWwIKhUKh2D/UfVhxuLAvzWzFKnDJ/sCuRIuCaIaQMXGFeW/YnvgpuhZESh8AicSXucJn0sL2hwtpGGOaBHdX5Z4uigkS92x8nE2pHoKayYJoExJ4tOuV3VZmNyW76M8nMTQdSxg40qUzO4Tte6VjVg1sYcjJIJGYmo7te2xMdOFKH4mkPT2AIXQyfh4BOL5HQDcRSIKaOS6N4nBOrRjLQfEgH2iU902hUCgOLeo+rNhb9sTfO5WAnszrKxEE9No9Et1bhu9n68gPcPwEphZjbvxTzKu+HIBnO5YjpEHa3YJE4sksUBDLGiE0oRMx52Fp1aSc7cQDR5euWR04heH86oOaYlHkhtUPjLNTpN0cNVaU2065bNLHve+J28h4eSxtV63U9l3CeoDfvueG0rnb03105YbRhYbv+6Q9G4kkqgfQNA3bc8n5TukchtAwhM78aBMuPved+dnSvql80+XHHQ6oSXoKhUKhUCimnd1FtpULaIHBQPZFerNPEzePZ2HttZN6ffN+P22xa0vCenvipwDjBOqW4fvZMPxNNAx0EcKTGTYMfxOAedWXl2LhIuY8kvabo48SgMDQQvjSJed24mlZHH+oIrWiM/3wHmUzTwd7E5VW7v9NOlkAXHx0IfCkBAnBMsHcmR2kKVhNSLfozA4z6CVL+3KeQ0hYaJO825TzHWZHGiq2HYjJeweLgxbzplAoFAqF4u3L7iLbigLaxyXtbAEh0bBIuZtYN3g7upjYb1z0JE8Us1Ye2bZh6C6QAk0URi1rwkLDYOvIDwBKsXC6MBGYGCKKLoJoWEg8pPTJ+4Ok3M2Aj4+LEAJDi2KIUEmYHyj2NDptT6PSxvp/A5qJL/2SNaIgcyXDTqZ0veK541aEllA1IBAINMBHknbz5LxC9VggCOsWUSOIrml050bGRcft7+S9g4kSyAqFQqFQKKad3TWzFQV01ulACB1B4Z+ULoYIgWTCXGMkpcp0uWDdMHh3hXD2sfHJ48tddgCBieMngMqGQCE0hNCImguIBY5G+hKPNAINjQCgkbI3Y/vDwIFPrdibEcx7KjrHZiPPjtShaxqaEMyJNOBIDx9JW7i+dL3yZr+u7FBJNEaMIGG9UD32kRhoBIVBznMYdjKk3TwpN8e9mx6vWHPRN11jRRmwk9RY0cMi0m0ilEBWKBQKhUIx7exucEdRQHsyh0AHQOKhi+ColaIPnTAJewND+dVIBMfWfgmPzISV6aSzvkI4a5gAo413jJ7fwdRijMXSa5HSw/OzmCKO0DR0gsQDizC1CBIPT+YYyb/OSH4tOa/7gKZWTDTow/U9/vnVB8dVlPdUdHZmBwnrgdLXcSvCUZEmPOmzPdOPJQzmR5upDkRLg0VeGW4vnTvl5gnrASzNQAiBqRmEdAsB6JoGQiDZ1damIViX6ODWtb8eJ5JvO+Wywy61YizKg6xQKBQKhWLaKVZoy5vwFpQ1sxXTIAQGvnRHH+UTNFrJed3Y3iBBvZ6awCmjDX4ZgHEjpYFSpVoXEWx/mFR+Kz4OIPHx8XwbIcDHZX78U0BlE2HEaEMXAdLODjJuBz45DFGFlBJDi5F3Byn4kyWezJF225kR/cABe63G+opH7DSd2SEksLCqZVxm8Z6Me57I/2vqBqfVzS9dr7x5ruhjLp73teF2Um4OSzNwfI+0n0dKiYaGlODgVlxPGxXRI3b2sEyp2B1KICsUCoVCoTgoTDW4oyigNwzdzUj+dXQtSFifgy5M0s42gnrzhA1+k8WsVVlHk/O6STvt+NiADqMiziONIWMsrLm2lGKxPfFTfN8h7ffiyRzIgtNWFyEMQvjkC95oWagmF84JuggS1GoYzq8GLt/r12Ts4Izls5eOE7Od2WGEEIQ0s1RRhr2LR1s+eyl3rX8UoGJq3VWzl/Jg+4oJm+dCmsVVK+7ljUQHxmiKhS188r6HpekYmo7juwjAl7vqx7rQkICORl46EzYMHu4oi4VCoVAoFIqDRnnj3KqeL1QM3agLncYZrT/ktObvUBc8HSlcLL0OU6shZLRUnKfo+x07TMTS6zi29kssrLmWrNuNxKHwhj8IdHQRwRRx6sPvKIljgJH8erJeF560EZh4ZPBx8GQeU4/jySyuzOCSQCLRRZCYtYh4YBEho2WfPMiTeY3HDvrIeDmQ0BKqKT12sqSKyZjKijGRj7k/l2DATrIh2YXne2Q9G0d6OL5HIRtaMjfaSJUZwtB1DKEXIt5GX2sNgYePKYzDMqVidyiBrFAoFAqF4qBQtDFMlDhRfszYLOR44OgpG/zqQqexuOlOjq39EgDrBm9ne+KnaARHq5o+gsIUPl0E8KUzTtD6MlsQdqLgsZWlR9rkvV40EaQom3xsLK2xMDiEfZ+cN5HXeKz3d8BOEjVCtISqqbZ2ea0PZDxauXhuT/exMzNIv51iIJ8i4+XxkWhCIITAQxLWA5iaQbUVKYh2CSDR0fCQeNJHQ5BycmS8PEP51KQpHIcrymKhUCgUCoXioLBXWcjSYCD3Ir2ZpwmbbXh+bsqJeeWPLYpvnxy6CBXSJ0RB8kg8NGGOE7S6FsJ100jhjSZoCCQ+4OPKDBo6ugigiQi+b+PIQaScuV+T86bKMC73FRcrzWk3N84eMZFFo9x2Udy/MdHJkJOhORinKVg9oY8Z4K71j1KvW6xPdJL3HCTFNGiBADwK4twUOqsHtxLUTeJmCEf6BDWDhJMl5zs4vkdIt5gTaQBBxbV2t+bDASWQFQqFQqFQHBSKwz7y3lBh6IbMoYkgutsP7BLQnnRIu1sADU1Y5N0eTD2OlIK87B/X4Ff+2HLxHTKaSTs7QPh4votHnqKvuDpwSsXaYtbRpEUA2xscTdIwkRTHLmuF9j6ZJWjMxTAipJ2t5P2J11LOVNMB93RwRrHCWy4qrxqNcbtr/aOEdKvColEuRP/19V+RdHOMOBkksDmVozeXYHaknpBuVfiYyyvaIT1AznMK/mIkmpSlqroc/Zf3HDJeHoHgQzNP50vHXwxMPN2veP7drflwQQlkhUKhUCgUB4WwMZOUvZWc10XBpWriyxy+tBnIriwJ6LTzBpRVfX1pE9TrsfRaFjf9cMJzTzRpL6g348k8mgyQ8jYBAkPECOqNdKYfJh44piRWqwOn0J99fjRaLownk1BqO3MBA10Ecb0RAmY19aEzWNx055TPd6Kq9rrB20tT96ZqnBvLREkVN6x+oCRogXHNe/dsfJx+OwVy1zORQMrNsTXVy5xIQ4WPubyi3RoqVJmLj/NGx27raOhCo5CaLDGEji4Ej3Wv4f+0nMDiunlTVsbLRfhEaz5cUB5khUKhUCgUB4W22KXkvG6g4PVFFERXyGhme+KnpSxkV6bxZAbHH8HxEwih73YYx2SDSGLW0UQDc6gNnEJj6CxqgycTNlsrpt8NZFfSmX6YkNGCTgjHTyLJI7DQiVKsICMFtp8kYW9kJL9+XJPhWMqr2hNN3dvfwRljs42hsnlvY6obQ+i40ivtL1aE857DukQHA2X+4LFT+QyxSyZqCExhoAlBQDOIGAHiVpioGSSoW3jSL1WIi+cZttOsGdzG830bWNG/ge7sMBsTnVOu+XBBCWSFQqFQKBQHhbrQaVh6LRoBfGmjYRK1jiKoN5Nxd44K6H48mR99O7/gA/Z8e7fDOKYaRDJ2zHXeGyLlbKc38zSrer7AhqG7MUSIkNFCdfB4LK2KQiycj65ZCCwK7uUUEhtDVBE12yZsMixnovHannTozz5fSvFoCw/s8+CMycZMh3WLq1+4l5SbI+lmcWRxlPSuSnKh/iuJm6HSlL7yNIuOzBCmbhDUTKr0gggOGxYRI4CLh14mIT0pCetWSeQun72U/lyCDYlO0l7he+kjybg2/XaKntzwuDUfbkkXSiArFAqFQqE4aMSso4lac6gNnko8sAhLqy6lQNSFTiOgNaAToijldMJoQifrdpem7k1EMe5NSsFQfjUJewO+9NgweDcZZwdD+VUMZlfTn32BhL0W109jiAi2N8BI/nW8shHUnsyhExoV5zkYjYoD0AjiyhFsf3hcRXgsY6vatj9Myt6IEPqkKR57w8nVs9mY7Oblwc28MbyT7uwQA/kkO9IDrEt0jAauFZBQ8bWGoMoI0RyqqfAiFyvaaS9PUDOZX9XMSbVzOKV2Lovis6gyguhCx5bu6B8iPr70qbEiJZG7uG4eDcEYfvF7KDSiRpCgYaIJQXduZLejsQ81yoOsUCgUCoXioDHZYI9iCoRHhtrgqdj+cGUjn4hM2ghXZCT/JklnA1K6CAoDRjSho4sYtt9DMY8BJJI8ptGKoUXRtSAZp70U26aLIB45TFGF52eRSAQaEg1DC+FLl5zbSUCvmdL6Mfa5pu3tIARhY3bBcjEmxWN3lKc/hDSLATtJczDOkJ0m49l051xagnFG/CymZmAInbSbLwnV4sewbiEQzI7Uj369y+JQ9DpP1GiX8fIsiLVycvVs7t38J/K+S1i3qLFiGJrOydWzuWH1A3RmB+nIDGIInaBh7prQJ8GVHnWBKmqsaEXD4eHkPwYlkBUKhUKhUBxEdjdyujg6OqDXlASr66ew9LopzzuQXcmm4f8EJLoWwvETozFtBq4/ONp4l4NSMoWO7fYTMWYS1meTdDfg+il0EcHSa0m77YS1FrKyC4MghSxlHUkhBq5wrqkzkMc+V4lH1Jhfel7AOIE9WQRaMeqtmP6wdmQHed9hfrSZ5njhfGk3x6ZkN570CQgTNIgYAbKujYePJjQCmoGlGcwM15VylSeyOEzVQLi4bh4LY60V66wzI9y7+U940iOkBfClxPbdgmdZNwuv1ejgkPlVzdx2ymVTfj8PNUogKxQKhUKhOKhMNXK6vOrqSYeM045Hjrh5PAPZleMeV4xR688+jyszBXuGAEarvr7MI/ExCLDLgWsgAEcmsf1hdM0kbh6PpdeScXcSMecwI/oBhvOryWW7EWhErLkApOzNgIcmgiWf81QZyOXPdVXPF7C9gYr95QJ7rAguj0Abm/7g+B6G0OnMDhMfFbphPYAETGHg4aOjYWg6ISOALgSLa+eVhK+p6aNT+iZOzlhcN4/zW07m/m3PknCyxMwQl885q1TpHZvT/Herf4xEEtQsXDxc6aEhyHkOutABiSs9GgKxw85OMRHKg6xQKBQKheKwoeQlRpC0N4CAKmMhQsgJp+4VJ/NJWZiW55HBlw5iVOJIfDQMPJmloJx1BMVMX0Ha3o4rsyysvZbFTXdy1owHWdx0J/HAMQBYei0SD9fLYIo4IaMV0EYrzXWlyLY9YapGQph8sl6xUlue/hDUTUCQ822G7TTrRnayamgrhtCwNB3Hd3F9D9f3caVHlREsVaP3JDlj1cAWHu16hbgZIqIHSLlZ7t38J3629dlxz+vB9hV40scUOoiC5zigmwR1C0szSvnJx8Zm8I+LPnDY2SkmQlWQFQqFQqFQHBaUD9XIu/2EjVmEzdZdB4zx65bHqGmagednKUy+S6IRQpIDNIL6TDLe1tGTaGUDQMCVKVojF1aI3PL84ojRhsQj7W4l5W4joNUwv/pvmVd9+V4/v93ZS6bKDy4OFXF9j87sMCk3h+N7mJrO1lQviIL8rw9UYfsu1VaY7twIEskxVa1cteAvAEoe4dZQ7ZSRcg+2r8D1Pbpyw6VmvKx0uHP9o9y/9RlOqGkrCe6CeLewfQ9j1G+so+Fgc2bD0Ye9nWIilEBWKBQKhUJxyBk7VCPtby1YJrQQAGlnO65MAZLnOz7JwtprS8NBbH8Yz7dHz1RowvPJIbCIGG345Cm8ae5B2XQ8kOgiOG5oSLnwtv1hHG8YXQTRRJCIOWvc8XvDVPaSqSbrLZ+9lFvX/pq+fAJD6FiaiZQSx/eQAqrMEHEzxJCdIePliBohbj/l40BB7N669tcM2ulJR02PpTM7yGA+jZSSvF9IrCh9r+wU7em+0uNbQ7UFMZ0dxvVBF4KcZ+NIn42JTm5Y/cBhOU56KpTFQqFQKBQKxSFn/FCNCAJI29tIOhtwS5PtNBLOG7zadxM6YTyZJut0IIQ2aqvYFQ9XbZ3Aovp/wNAi6AQZL3sEAn1cVFt5fnHh3DoCE9dPk3a2k3a28Urvl/c5nm0yynOIx0agLa6bR51VRUAz8aXE0nQWxloJ6CZRI0hrqJqe/AiOdAkIk5Sb419f/xW3rv01Q3aKlFPII+7KDjPiZCrsGxPRGqol6+dxfK+U+1F4xQoM2ZnS45fPXoqh6bSEqrE0nYxnY/seTYEYsyMNJTFeHEhyJKAEskKhUCgUikPO2KEaIXNG4a19UvjSRYz+z9DCCAxcOQICXJkl7w/jyTQSFwBBAIQg5/WVhLcQAp1w2RUlhoiMjpauTJIozy/2ZA6BPtrsZ+NJG40Arp/crwzjididPzjr2yyKz+KU2rkcG59JtRUhrFtk/Tyd2WF0URwDXRjckXRzjNhZIkaQvHSwhIEmNLqyQ4XnOcUEu+Wzl6IXp/CVjaoWFDzGOd8uPb7Y0DfiZEm5hcElzcE486qax3mpjxSUxUKhUCgUCsUhpxjvZogoAJZWTdicQcrZMuoZNtC1EBqFpi9fOngyQ2vkQhL2v4+epZhz7CKlhuMN0599vtCSJ+2yGqgEJFJ6GFq4IkliILuSvDvIiLO2UHWWGj42/ujoaQS4fqGanXU62TB0N2eEfnjAXofydIixTGTBqLEi5HMuGS9HQJi4sjC4oyVUz9ZUb6nyG9QsHOmO2h8KQ1F2N8GuORBnk5vDxS9tK1T3dYKaVXp8saFvZriWsB5g1dAWRpwsw3a6FCVney4vDmziiue+XRFfd7iiKsgKhUKhUCgOORMlPAhhUB04GVPEMbQw2mhdr5BFDHm3fzT7uCCMBToCHQCfHB6Z0W0aEoHEZlctFDyyOH6KYXsteXeQLcP3s27wdoSQVBkLQYBHDl/6MHpu10+NVqoFjkwwlF/FluH7D8prNJEFw9B0rjzqHKJGiLx0sTSdudFGqq0IpqZjisJr1hqqxpM+jvQI6uakE+xWDWzh6hfu5fqXf0S/naTRipUm8GkIDKEjgBorXHr82PSNsB4EQalSPWyn2ZLuRUeriK+byHKxamALN6x+gCue+zY3rH7gkNkylEBWKBQKhUJxyCkmPFh6HXm/vxShtrDmWkw9ju/b+NId/ZfHlXlMLT4a71YuZ3wK8kZgiAgRq210m8euCnMh7q3grPVKMXKbhv8TKV0MLUrAqKEmcBI1gROIB44rjMQmM3ouRkVy4fNNw9854H7kiZjMgnHJ3LP4lxOX0xapZ3aknrgZJu3mqDKCxK0QaTdHzAzTEqxGIEan342Pd1s1sIV/ff1XvDrcjuO7pNwcI06G2eF6InoAicSVPq706c8nSTs5HmxfwaZkF2E9UIqbS7s5sq5N0smyM9XP2pEdBWHv5ejKDk1quSjmQA/Zqd0K6elGWSwUCoVCoVAcFkyW8HBC/U1sGLqbpL0eAI0gYaORsNmK4w/h+hKP3OhIaDEqXiWmXoulVRMwmkg7m0fPJoiY83C9ETxpAz4BozCJTuKRdwcIGS2la+siQt7v5+TGr/NC95VQZjco4OPKTEX8XHlcXdiYSVtZlNv+MpkFoyiey6fbXTW/EO1W3DY70sDfHXvhpNaGezY+Tr+dwpeyUDWW4ODRkxtBCAjqFvMijWxJ95Jx8/hS8nz/BvKeQ2dmCA+JjihkNGs6ed9jW7YfKFSfJbAt3QdAS6hmnP957DCU4scH21ccdDuGEsgKhUKhUCgOa+pCp7GQa0uiM+PsQBeF+LeQOaMw3U4agI1EoyDHdDLudrLuTgqitmC9EAjybg8+Hhomutjl59VFGM9PV1y76E+uC52GKaI4cmiCFbokRsX72Lg62xtg3eDtezVQZF+ZSjzvCRtT3RhCxxVe6Y8NISVZ3yZiBAhpJt25YTzfw5YetptFR+Aj8ZEFd7cQZDwbSzMKdX2hFcSxLJzPF7AzO0jcCo/zP0+VA32wURYLhUKhUCgUhzXlE/MCWj0CnZS7ibw3VKoQQ6HxTMMgqDeVIt8kNrJksfDRsAANKR0kLkFj1yASS6tBCHPSSXemHitbVdGqUfi8MKRkori66LgYuYPJ3nh6C89GEtTNYhvjLse2LFR9024eW+4atOKXebrl6P9pQmBpRmGflARGP3ekhyd9bN9lIJ8c539uDdWS8fIV23bXSDhdTLtAnjNnDieccAInn3wyS5YsGbdfSsl1113H/PnzOfHEE1m1atV0L0mhUCjeVqj7sOJIZ6zojFhtICUZt528N0TO6QRAJ4quhch7vQihl8W6CQQGGkEMLYzER2BhaQ3owiTvDTGUW0PG20FQa0YiKnzQxcpvzDqaXW++S4q5zAINbbSiPTauDhgXI3ew2FtP74KqZtxREQsUEjEo2C1aQtVUW5HRhsUCxTyQIhqCmBWiygjh4aMLHYRACFEutQuPk+WPLDBVDnT5czoYTXwHxWLxxBNPUF9fP+G+xx57jI0bN7Jx40ZeeOEF/vZv/5YXXnjhYCxLoVAo3jao+7DicGcq325xYl4RS6smai0g7Wwl7WxF0wIIaQESgY7Ex5c2ugihSQtNBNGEgS9t4oFFoxXiwmzmEfsNPJlDJ0SVuRBdM3FlZkJLRFvsUvqzK/CljcQbTdMQBLRm4oGjgfFxdUBFjNzBZG89vVfO/wu+8uqD9OeTSCQGGrqmETNC5D2HtJtDlKrmoKHhl9WQhRC4vg9IDHQiVoDBfJqMm0eM1vR9YE64nupAZNw6JvRRl8XBFQV/SLcqBP9UI7P3lUPuQX7ooYe44oorEEKwdOlShoeH6erqoqWlZfcPVigUCsV+o+7DikPN7ny7E4lOXZjUh86oGDeddrbgSxfQkLijw0M0pEzjSwtDhHH9FFm3f7TprB6dEEKMvqEuwNCi4MOGobsJJGrHCfb51X9bSLtAQxdxLK0GTTNLNoy22KWsG7wd/ELl2JNpXJllwej+6WDVwJYKUVnMGN5bT+/iunnMCNVi+y6OX4iDawnVYGqFaLdqK4qpG0hX4uIjR6vLxea8mcFaRtwMWc9lUbyZK+f/BRsSnXxrw+/wpY8hDGaHapgRqUNKOeE6psqBPphNfNMukIUQ/OVf/iVCCK666iquvPLKiv0dHR3MmjWr9PXMmTPp6OgYd2O+9957uffeewHo6+ub7mUrFArFWwZ1H1Yc7pRbKICCEPYpJUNMJTq3J35a8CbrNcA8cm4nnsyONpmZGCKEJ7OFQR9aDInA9RO4MkPW7ShN32O0qS+g1+BJh6S9gerAonGCfV715cQDx0xa7S7G1ZXvX3AAUyzGMlVVdaLBIhkvT1i3uGH1AxWCGgpC841EB1EjwMxoXWnIx1A+xfZMPzPDtSysaqYvlyCgmwzZaTKeDRJmhGowdJ2jYs0VQ0AW183jleH2Cdext97ig9nEN+0C+dlnn2XGjBn09vZy7rnncswxx/Cud71rr89z5ZVXlm7qE3noFAqFQjEx6j6sONwZa6GASt/u7kRnUTxbWjWelsX2hxGYgI8ns5haFN+X2N4gea+XSudsEQ/HT2D7w2ScdnQtWCHYbSfBK71fJmDU7za6bbK4uulgqqrq8tlLuWv9o0BBSGa8PAP5JFIWHMF1VhXt6T4+//J9uNIjYgQwhU7Od9ia6mVutBGALeleLGFQZ1WR8fIIIagyQhiavkdT8SZaR9azuWpMk97umEzwT0cT37Q36c2YMQOAxsZGPvCBD/Diiy+O279jx47S1zt37iw9RqFQKBT7j7oPKw53wsZMPDlxvFqRutBpLG66k7NmPEjbaOX42Y7lbE/8lNbIhVh6HSlnOzmvB9AxRLTkPZbSwGEIicPU0keStrfjkSOszy5tzXtDZL0uXD9ZUVE+GMNBdkdndpCwHqjYVqyqTjRYpM6qoj4YI2IEGXEydGWHsX0HX/p4UuJKD9f3SpPwdmYGAMnsSF2hQdIIUheooiZQGDQCcNf6R6dsmJtswMne2iL2pInvQDGtAjmdTpNMJkuf/+EPf+D444+vOOaiiy7ivvvuQ0rJihUriMfjyvemUCgUBwh1H1YcCUw0Zro8Xq2csZFvtjdAZ/ph2mKXEg8cTcxagKVVgfDRhAFo2LKn9HixG+kj8Yibx6NrZmlbzu1EAIYWmTK6bSC7klU9X+DZjuWs6vnCQRHQu4tGW1w3j9tOuYz7zvwst51yGVnfLgnqruwQmtBG6+kCQ9MwNYOAZhIQJik3jyd9joo0Ebd2JXOE9QCbkt17lZAxdh374hk+UEJ7T5hWi0VPTw8f+MAHAHBdl8suu4zzzjuP7373uwBcffXVnH/++Tz66KPMnz+fcDjMD37wg+lckkKhULytUPdhxZHA3vh2p/IrF60apeEhAsToSOli1Fvx80qbhUAnhCYs6kKnj/M8uzIDUhCydr2zMja67VANCCm3L9ieS0d2kKznsCg+g1UDW8aJx3KbQs5zMDUdTexKptCFwPE9jqpqosYqvMZDdgqAETtNZ3aYjJfDk5LZ4ToigYkb5iZrHNxfpmriO5AIOVEQ3WHOkiVLeOmllw71MhQKheJti7oPKw4Vz3YsLwwLKRN1Ukryfv+utAstiu0Pk3U6cP00Hnk0AmiaieenR6VxcdiFhkYQgSRgNHJC/U3UhU6riJ3Lu/1YenXFCGrXT2HpdSxuuhOAVT1fKF17smOmi1UDW7hn4+O8keggqJnMCtdi6gZZzx5XYS1v6mtP95PzHTy/kG1s6QZSFkRylRGkIRijL5dgyMkQN8MM2anC6y7B8V0MTeeo6K7qspSSATvJ544+v3SNcs/xdFV7p4NDHvOmUCgUCoVCsadMlTNcXvk1RRzNNHBllrh1Eh3pX4P00QjhkR19pIUmDDR0qqyjWVhzbUUaRfHzYnXY9VOTRrftrtFwOllcN4+a9ijHxWdUNLAlnSz//OqD1AWiFVXcYtZwfy5B3neZGa4lqJnsyAyS9x1mhWqxpYsEZkcaCOSG2ZruQxcaVXqQllANXdkhcr5DZ3a4JJCL1o6DGcc2XSiBrFAoFAqF4ohhqsi3qawaEXM2W0d+gCMTBLQ65sY/xbzqy8edf7KBJbuzgBzqASFjI9CG7TRd2WF8fBZWtYwbqlE+fKNohTi1bh7LZy/lwfYVFWkRzaEaOkabAY+N73o+W1O9ZLxCw1x5MsVd6x89aHFs04USyAqFQqFQKI4YdidWJ4tYm1d9+YSCuJyJfMSv9d9EQGvAI0PYmDmpp/hQDAgpZ2wEWld2CASEtWApfQLGV3En8vROJHBDWqCQeTxKtRWhJVTNiJNlwE5WTL07mHFs04USyAqFQqFQKPabqUZFH2imK2d4w9DdZJ1OJC66CGLocfJuL442QrV14pSNdwd7QMhYxmcN2wigNVRdOmZPq7gTCdzaQITunEvazZV8xYam8y8nLh8nsA9U7vGhZNpzkBUKhUKhULy1mSh67XDJCd5TBrIrGcm/jsRHExY+DhlnOxKBlO6U8W5FyrOaFzfdedDEMYyPQIsaQVpDNRXxbHtaxZ0ob9j2XZoDcTYmu3l1eDsCJm26O5hxbNOFqiArFAqFQqHYL3Y3KvpIYHvip+haECkLiQ4CHYmPxMYU1aXjDlbj3b4w1lt81/pHKyq+e1rFXVw3j/NbTub+bc+ScLKEdBNTaNRFqmgKVZPx8hV2i92t5UhEVZAVCoVCoVDsFxl3J7qIVGw7nIXkRGTcnaPT83x86VJIwdUAj6DRWjruYDbe7Q/7U8VdNbCFR7teYWa4liW185ASUp6N43slP3NIt3iwfcVBeCaHBlVBVigUCoVCsV8c6gSHvfE/T3Zs8TlEzHnk3E48mcMQQXzpoQsTKeU+Nd6teWotD939O7q39NI8r5GLrz2Pk5YtOlBPfUr2tYo7NqbNxcMQOl3ZIapHLRtHWirF3qIqyAqFQqFQKPaLvRkVfaDZG//zVMcWn4MuTGLWccSsowmbs1lYcx2WXkfe78fS63Y7Ga983PSfXv8bfvH//oPh3hFqm6sZ7h3hni/ex5qn1k7nS7LfdI5GuhUJahYgyXlOaduRlkqxtyiBrFAoFAqFYr8oJjjsjZA8UJT7n3fXSDfVsZM9h3nVl+9x491YAd7bvY3j//pNmk9JIDRBJBYmGA3w0N2/m86XZL9pDdWS8fJlX1fjSg9T00tNe1nPZvkRlEqxtyiLhUKhUCgUiv1muqLXJqLcJpFxdhAx51YImsn8z7ubdre/z2Fss2J2SBKMWzSfvYHk5mYAwtEQ3Vt79/kaB4OxMW2GptMQiFFnVY3LPH6rogSyQqFQKBSKw47JvLtjh3lkRScpeyMisJDcsE731j48mUZ4Mao2ra3w+063V3pgaCN9mx3ymR0EwgE0Q8NOS0J1qdIxmVSW5rmNB+R6k1E+Ha98xPSeUmzwu2fj46wZjXRbUNXMlfP/4i0tistRFguFQqFQKBSHFWueWss9X7xvQu/uWJtE2JgNQjCS3sL2tTvwtQyBKsmW384a5/edzCvtbD2DW5bfwSeP/v+4dNZVfPLo67hl+R175RVe89Ratr+cxdeymAETx3Zwcg5Sy5HuCSF9STqRIZfKc/G1503HywbsincbslPUWVWlEdOrBrbs9bmyvs2CqmZOrG5Dwj6f50hECWSFQqFQKA4xK7raueaJ33DB//yIa574DSu62g/1kg4pD939O4LRAJFYeJx3d2ykXECvIWrMx7HzhOoc/GyEbb85Fae7bZzfdyKfsbnjI/zosy+xc0MnQz0jZJJZhrqH2bmha68a6h66+3d0PrUAKyzRgw66oRGshmBMMPTyCQz2DFPdGOeqO66Y1hSL8gSK/YlkO1DnOVJRFguFQqFQKA4hK7ra+drKJwgbJg2hCP3ZDF9b+QT/dNp7WNoy+1Av75DQvaWX2ubqim1F7265TSIxmCxZKkY6I6z/wdlUN8TGPaacsT7jW/6/OwhGA3RsHEQ3dHRDx3M9RvpGmLGghYfu/t0eCdrCmmez7TcBms/eQLAuRa4/yus/beVr931j/16QvaAzO0idVVWxbV8i2Q7UeY5UlEBWKBQKheIQct+bqwgbJlVWIVar+PG+N1e9bQVy87xGhntHiMTCpW1F725b7F2sG7yd1FCaHWsHMKM+gbBk3S+baF+7g7nHzyJeH6t4zFQUxXguY2NaBVmkGxr5jL1XDXXFNYvNzaWGvHQiQ3VjfF9egn2mNVTLkJ0qZRjDvkWyHajzHKkoi4VCoVAoFIeQ9uQIEdOq2BYxLdqTI4doRYeei689j1wqTzqRGefdLdokujfkKiwVevpohISOTd175fdtntdIJpUlGLbwvcKYac/1CYStvWqom2rNB5Pls5eS9WzSbm6/ItkO1HmOVIQszFI8oliyZAkvvfTSoV6G4m3K3kxsUijeqqj78IHjmid+Q382U6ocAyTtPPWhMP/5nvcfuoUdYkopFlt7aZ67K8WiuP3F364iHA/RPLeJeF3BCjDcn2Dnm520HNVE89xGjn/nMbz+5zcnnGJXPM+WNdsZ6UsQrQ2T6B9Nm5DQNLcRw9D3yjM82ZoPNvubYnGgz3MkogSyQrEXlMcL6SJSGjt6sALxFYrDBXUfPnCUe5AjpkXascm4Dv902nuAgtWiPTnC7Ko4Vxyz+G1ru4Bd6RbBaICdG7rIZ/IIBLMXzSReV1WyNHzlwS9WHBuOhsiksuRSea664wqAin29Owfoa+8jEAkgEAQiAead2DahwD2Uo6MVBw8lkBWKvWBVzxcKzSHargxN109h6XUsbrrzEK5MoTi4qPvwgWVFV/s4IQxMKpzfriL5luV3lLzJI/0Jtq/rACkJhC1mLmwtCeCTli2qOLZIuSd4sn1fefCLk15/KtGtRPJbC9Wkp1DsBbubwqRQKBT7wtKW2eNE7zVP/EY1741hy5ptZJI58lkbXdfwPQ8755BL55l1tKgQqmOTMEYGknRv7WHDS5sxLZOZR7dWnLu8IW+yKnF5/BxQ+rinSReKIwclkBVHPFuG72fryA9w/ASmFmNu/FPMq758v887kfdquqcwKRQKRZH25AgNoUjFtrdz896ap9Yy0pfA9yVCCFIjaQQCK2RiBS2yyVzF8eVJGCMDSdrX7kQiCcfCeK7HtkkSL8qrxOVDSq6644op4+cUby2UQFYc0WwZvp8Nw99Ew0AXITyZYcPwNwH2SyQXJxGFdKtiEtHfzns3nvwx+FR4kBfELj1QT0mhUByBTGSR2N8q7+yq+LjmvbRjM7vq4MaGTTd76ul96O7f0TC7ga7N3eQzNlJKJBI7YzP3hDZy6Ty3ffxb1DRV0zyv0KD3+P1PA9C9tQeJBCFontMAQrDt1XY6NnUTq60qWSUuvvm8cVVi1/Hoae/jpg/cTjASwHU9mmbveifxYIyOVhx8VMyb4ohm68gP0DDQhIUQAk1YaBhsHfnBfp13sglCD3Wlxk1hUg16CsXbm2KTXX82UzHoY3+n4V1xzGIyrkPSzuNLSdLOk3Gdkj/5rcBUI6XH0r2ll0C4EIdX3j4lgfRIhp6tvaRGMtQ2V7NzQyf33/wLurf3sv7FTQz1jODkXepba4jXx4jXVdF2wizsrM3a59az9dXteJ7Hj776c1787Sp2buhkZCBZqjz7no/vS6rqonRu7KKnvf+QRrkpph9VQVYc0Th+Al2EKrYJTBw/sV/nnWqC0NgpTAqF4u3Nng762Nsq89KW2fzTae95y6RYlFeKQ1VBpJRsfbUdzdCYsaClNFIaJvb0Ns9rZM2TazEDJp7r4fuS0aIwXZt7MAMG4WiQxGCSnu39uLaL57gEwgFcx0PTNfo7h4jEw8TrY+QzeQxTZ+6Js3Fsl+2v7UAKMIMW+YxN+9qdaIaGZhRqicGISdPsBgCSA0kMUy9Eud2sUizeiiiBrDiiMbUYnswg2BWyL3EwtdgUj5qaNU+tpXtjH29Gu5CGTiQQYG5dPZYu3jYThBSKg8GrK7fw8E+ep3vHEM2zarjwY2dw4mlHXsbqnniF93Wc9ETNewea6bCHjKXc16tbOhtf2oIU4Hs+ljBpX7uTupm1pIZS5NJ5NE1jzVNrK4Tnxdeex4u/XVXwHIcsssksjEayZZM5zIBBU1sDPdv70HUNx/eRUmIFLaSU2Fkb3dDo3taHYRn0tQ/QMLuBSCzMhpc3YwQKkqhoxZBSkh5JE4mH8TzJzLaCOG6cWY9hGvznyq8f0NdIcXihLBaKI5q58U/h4+LLgh/NlzY+LnPjn9qn8615ai23fuMB+vIGWALh+6TSWdYNdNGfS71tJggpFNPNqyu38L3bfsvwQIqa+ijDAym+d9tveXXllkO9tL1mdlWctGNXbBvrFS6vMmtCUGUFCBsm97256mAvt4LpsoeMpdzX27u9DyNgYFoGcrThzvd9OjZ04eRdNL1QtR1rtThp2SIWnnYUQtNAQiQeIRwPIT2JaRk0zWkgXh8jl7HRdA3P9dH0gswJhCzMgFmYjjca5xZviNE4sw6AfLognjVdQ3qStmNnEAgHAIHQNNqOnbFX46sVRz5KICuOaOZVX87C6uvRRRhPZtFFmIXV1+9zg95Dd/+OnYvDmGkLfWcQ4WloJvhpScipf9tMEFIoppuHf/I8wbBFOBpEaIJwNEgwbPHwT54/1Evba/bEK3y4jpM+WMK9e0sv4WjBDlcuRnVDw/N8nLyDLwtjnqUnmbGghWA0wEN3/67iPJ+4+aM0zW5g3kltHLd0IXOPn83Mha38zb99DMMwSCcyBEIWTt5BCDADhdfcc33CsRAzF7Zy+vmL+cqDX6SutYZ1L27klSdeJ5PKMtKfJD2cRtM14vUxZi5sYdE7j6ZpdgPGqJhXnuO3D9MmkHfs2MF73vMejjvuOBYtWsQ3v/nNccc8+eSTxONxTj75ZE4++WRuueWW6VqO4i1MPHAMNcGTiVrzqAmeTDxwTMX+gexKVvV8gWc7lrOq5wsMZFdOeq7uLb1k4waGC1rKxNgawVxfhVxnMpw54mbqKBSH7b24e8cQoXCgYlsoHKB759C0X/tAU/QK14fC9GXT1IfC46wTe1JlPhQcLOHePK+RTCoLQCBi4bk+vlcQrW3HzihEtyEwA0ZpKt5k8WnBaICtr7az5qm1tL+xk0wyy+t/fpO/uPxdAGSTWXLpHLpp4Hs+mWSWdCJDajjD1lfbOf6dx7DmqbUMdA6SSxUylZEgfYnn+uSzNj3tfeRSeT5x80e56o4rqG6MM9gzTHVjXA0FeZswbR5kwzC44447WLx4MclkklNPPZVzzz2X4447ruK4s88+m0ceeWS6lqE4QhnIrmR74qdk3J2EjZm0xS6dsDGufPRzQKvH9gZYN3h7KVlid/vH0jyvkdBIDidiYLqFbZ7roVVZh/wXmUKxLxyu9+LmWTUMD6QIR4OlbdlMnuaZNQdtDXvLVF7d3XmFrzhmMV9b+QRAxVS8Q51IMd1RcsXGvC1rtjHSl6BhdgONbQ2lhrgZC1owLINwVYiG2fVTxqeV+5hrmqvp2NhFPmvj+ZKdGzrZ+up2pJQsOHUeTt6lc3M3qaEUIDAsnUg8RHVjNY/f/zTP/voFaltqGBlI4nv+aGScQCDQdY3kQIobfnxdSQgrQfz2Y9oqyC0tLSxeXPjBr6qq4thjj6Wjo2O6Lqd4C1EUtbY3UCFqJ6r8bk/8FEOEMLQoQggMLYohQmxP/HSP9o/l4mvPY+aqDLbwcQxwXQ9bSKItsUP+i0yh2BcO13vxhR87g1zGJpPKIX1JJpUjl7G58GNnHOqlTcj+enX3pMp8KJjOKLnyCLeZC1ppmF1PX3s/ycEUC5bMY8HiuXiuR3VjnMu/+hEMQyedyExqZSj6mF3bpXNTNwgQmiCXzNK1qYfOzT10bemlY2MXQsBxSxcSrYkSrYmw+P+cyNFL5tM0u75UgQ5HQ0hXEq2JEIwE0TQN6fsEo0ECYWtSUbzmqbXcsvwOrlnyZW5ZfseEkXSKI5+DkmKxbds2Vq9ezTve8Y5x+55//nlOOukkWltb+cY3vsGiRRP/B3nvvfdy7733AtDX1zet61UcHF55rZ1fPbyKru4RWprjfPDCxZx8wuwKUQsUptb5BbE7tuq7u9HPezsa+qRli/hHLuO7DzzK6vocTkOAY1qbue7sdx/yX2QKxf6yv/fiA3kfPvG0eXzmhvcVUix2DtE8s4YLrzt8Uyz2NMptKorHFavQRZ/v2McfjFSJ8jVNV5Tc2IEbTbMbiFZHqG6M85UHvzju+KNOnlOIgdvaO2F8WnGK3cbVW/Clj67rAPiuR963kZ5E6AIn77J9XQdtx4JruxXXKI6bziQzrHtxI8IQ5DM2ds7G9yVSSoZ7R0gMJPnVN3/LB69/X8Xjp5qyp6rMby2ELE/bngZSqRTLli3jxhtv5IMf/GDFvkQigaZpRKNRHn30Ua6//no2bty423MuWbKEl156abqWrDgIvPJaO3d/7wmCQZNwyCKTtcnlHK79zHtI1f4dAa0eIUTpeCkleb+fs2Y8WHGeVT1fKIx+1naNfs66XdjeMAGjnrzbj6VXEzJaSvtdP4Wl17G46c7pf6IKxWHCgb4Xv93uwxf8z49oCEXQyu5LvpT0ZdM8ctEn9ugc5VFv5TaL8krynhxzpHDNki9T21yN0Mru5b5ksGd4nyLSbll+B8O9I2xesx07m6eoXnzPR2gC3/UxTJ1oTRTP9TADBq7jAYVqcvm4ac3Q8fIunufh2G5h8IgETRMgBGbAxLQMbvr1lyqEb3ENRdEPkB5NxZhI9CuOXKY1xcJxHD70oQ/xsY99bNwNGSAWixGNFoTN+eefj+M49Pf3T+eSFNPE3jTCAfzq4VUEgyaRcKAwqS4cIBg0+dXDqwgbM/FkuuJ4T6YJGzPHnac6cAoJeyMD2RcZzr3OcH4tSWcTjj9Czu1FYJJ228m6XYyMpNm8cQtbN23n6R/VH5FxUgrFvqDuxfvPgWiy25PEiMM1Dm5fKG/MK7IvEWlFS8OWNdvZ+PIWcqkcru3hOYV/RTRdw7CMQt+ILsgkc1TVRLCCJm+s2MD6FzeRTmbIJLKApLGtnmCZB15oAt3UCcdCpZHSY1M0ytM4ikzWTKg4spk2i4WUkr/5m7/h2GOP5Qtf+MKEx3R3d9PU1IQQghdffBHf96mrq5uuJSmmiaka4YAJm+26ukeordkVrJ8cztC1c5CNWZtao55F799MVbRgh/BkGldmWRC7dNx1O9MPE9SbsP0hHD+BlDYCC12ERzORc1iijnRugKGeQXzHRNODNJ/5R1Z2rGBLx/tZ+2TgsBlUcN+LL3HvaysZ8nLU6EGuPOE0rjh9ySFbj+LIR92LDwzlTXa257I9OULWdTipvoUVXe17VN19Y6CXtOuQcx1ChsmMaIzqQKgiMaI9OYIpNF7r7yZbPC4S26dUiclsbAeLi689j3u+eB9QEJGZVLbgK755zyPSyi0NVbVRerb34Xt+xTHSl0hfEooGidVXMdg9jJt3MAMmp/7lSaz646tkEtnS44Qm8GyP/p2DzDpuBjvf7MTO20hf4nt+YcKeZRKKBscJ3+Z5jeMqyCoX+a3JtFksnn32Wc4++2xOOOEENK1QqL711ltpby80NFx99dV8+9vf5jvf+Q6GYRAKhbjzzjs588wzd3vut9tbe4c7E9kcXD+FRODJDIYIVQjdY2u/xDf/o4Oh4QyRcIDkcAZbvsIJy9YTr08hR+L0bWxkyfkC1+iluyPA2hXHY3knVNzgx153JL+WvD+AwMDUCmOifemiCYuhLoP1j5zBwvc9h2+beLZVqFLredqfeA9231FkM3lyGZvP3PC+QyKS73vxJb6++hlMoWFpOrbv4UifL59ythLJin1muu7Fb8f78Iqudv5j9bOs6e8mbJi0VVVj6vo4C8RYYbpw2QweHtnIyt4ONCBkmOhCw5OSaiuAI33qQxEihskbg72kHBtDaAQNA11o5ByXWM7kpDdieyx0izY2z/MZHEqTy9nous6nPnYmH37/+ASf6aI0XrroK75278Yyl1saNry8mUR/El9KBIAQpQqy0MAKWthZGysUQDc0muY0jE7Lq2ekb4REfxIJBSuFLghGChnc9a21bFvbjoRCo54sCO6mOQ0sWDyvwjpRLtjLRb/yIL/1mHYP8nTwdrwxH84827F8Qs/wUH41MWvhOOFs6XVovZ8reZA9uYbTL1iBnTeIBCBan0AYNonOZl5/8SzSA0eN8ymffMLscdcdzL2MK3OAj6VVl9bhk6d3Y4xoYwo9kMN3AuRH4oz0gmblCeh1bPqfiwHIpHJU10W58a6P7dFzP5AVmrP+6zukPJuAtuuNnbzvEtUtnv2bv92ncyoU08Xb9T58zRO/GReLlrTz1IfC/Od73j+uv6KdBK83DENMwxeSrFdoGosaFo7vkfc8FtbUE9R1NgwPkHUdADQECDDRcFyPeM7ipEQtb0ZGSOkOJ8xs4bp3vJOlLbMnvA/96uFV7OwYortnpDCQQ9OwHRdNCP71Kx84qJXk/aHcx/zqM+vIpbIgxK4q8qhv2Aya+J4sDAjRBIGgRTgeIjGQLAnpos8YClXkaHUYO+ewcMlR9O3sp7d9YFQ8F4aYWAGDm3799+OE7/6KfsWRwUFJsVAcmby4bQc/fmkNO4dGmFkT5+NLTuL0ObPGHRc2ZhYquWKXEC56iHURqTi2mCBx1gmzufYz7+FXD6+i7ujX8RyTSACqGgeRUkO6BsG6fha95wn+8LLNi31NhCMGrXqAXz28ipNPmD3uuroI4kob8LD9YQQ6Ah3QiNTm0QN5fNdE6B6hun7SyShuNkCgeddbl3szqKD8F2FtTYSh4Qx3f++JkoDfW4a8HBHNrNhmaTpDXm6vz6VQKKaH9uQIDaHK+1r5YI1fPbyKVI3Hmw0pUoZDTvfQHcg7LrFQAF3TyLoOGddBA0xdY0Y0xmv93ViaTk64haEVSHwJnvQIOxq26fFK7RCGLwi5Opu6+/nayie4pO44fv3Iajqb8+SO8wjmE6x9oJdgvyCdzhd8uaPjloWATNbmxlt+zamntB10y8W+UG5pCIYtcukcvusjBLua9HyJGTSxc87oaGlJIBIgm8rj2t6E55W+JDWcoao2ymDXEG3HzKKmsZqe7X3kMzZWyCJcFZxQ+J60bJESxG8D1KhpxYS8uG0Ht/3xaQbTGeqjYQbTGW7749O8uG3HuGPbYpfiymzBViElrp/ClVmqzKOnbLY7+YTZ3PKP76d1Rh7TDRCpTRbEsa8hfYFm+iQ9nZOOWUMAjbzw2BhMs26gb9x1U/YO8v4AYFMoEUgkLj42llZLrKoVN2cghI/0NXxfEGvMEKqS5Ed2NdnszaCCiRoNUzUe1z3zCBf8z4+45onf7HFGKkCNHsT2K2/mad/GN+Q+nU+hUBx4dtestzbZy5stCXKaR9DTcYRHzipMjfNGFZ1A4EuJJyVBvVCnyroOuhBogI9EExrG6LtjWcsnZXqkdQdP+Bi6jp/zCBsm33zteTbNSuOakqCn45qSTbPS9AazBVvFqK3GcT2yGRshROEdvtE/6F957fC+p1x87XnkUnnSiQyNsxuQfrEEXKiwFz4vvKbSk0gkuqEjhCCXmbq4IH1JJpGle1sf29/cQbw+xsJTj+KEs49l5sIW5p00Z1qfm+LwRlWQFRPy45fWELZMoqNz7Isff/zSmnFV5LrQaRxb+6WKZrxiQ91r/Tfh2JvxpYMmTEw9zoKaz1Y8vr56HjtTWxC6g/T1QllA+DieSd4xqKlKIhDMbezk5IWvUhdLsqpnHW2xSzm29kus7fs3Mt5WSu+dQelzjSC2P0hd1TwkLllvG9LzEFIjFPNw0i6bn1qE9GXJg3zhdXs2qGBso2FPIMObNQmk7XNUqLE0SGBP45muPOE0vr76GfBdLE0n7dtkdJdZ4XhpMME/PvN7Zo/EcPpdZtTH+eiykzh14fiqvkKh2D37YpHa3US8vlkuwgVTFISpIXVcPHRNI++55Dy3UPmUoxVd22YwlyFkmNieV3kXG/1CiuJHSdJ08POSaMAiYloM6Xlq/ACm1MhrHmnDxRUe2xe5tOR1xKCLZerkcg4IgWXpBAKFP+yB0jtyhyNFK0MmmWGoZ5hAJEAgZCE0gZ11Cl5h6RMIB/FcH6GNjov2fBIDSaS3ewep7/vohkbvtn5C0RCNM+v3qZlQ8dZDVZAVE7JzaISwVfl2f9gy2TlU2UldjHdbN3g7AMfWfonFTXeWBnpISeGv/NG/9l0vy4ahuyvi4E5s+wzNsyMgdcBDaJJAyCCVixEwHIYTVcxs6OCcU/9MJJgh60ZLSRkAjhzGEBEm+s/ZJ4dPlqzbRU2smbrY0VRVVRGMCoJWNUdVfQGROoahgRTVddHdNuiVx9m960OPEqjelRW7LjCInbDxRvJsWrUFmbT3Kp7pitOX8OVTziaqW6R9B9+AWZE4C+rr0YTAs336BtO84fVRFwszmMhw538/zcsbxlf1FQrF1BQtUkPDmQqL1O4qqhNNxPvgvOO4781VXPA/P2IgmseVHt6oRzZka/iAZgo0CXLUOmtJjUhex3MlmwYHaI1UYfsevpSERqvKHhIhQPdAyNEqqYSM4dLcVKhkC00gXEle80iYDr6QCCnwTfDOCpKp9cnnXXxfErAMNKHR3BQDIByy6Ore+3SMg8HYKXwtRzURjoZYcOo85p8yl9POO5mjT59PtDqKnbXJZ/OI0Wq57/r4fmXSBYIJv/Y9HyfnIoGBjkEGe4apboyrpjuFqiArJmZmTZzBdKZUOQbI2A4za3bZEaaKd6sLncb2xE8JGfUY2hwA8t4QKWcTKXsT1YETK44/qvHDbB65F8cfRmAQ0luxTB9dz/L8aydxytFrcDyTYACqo/0k7D4EBqu33U5WG8DNaFixgi+t8kaoIYC03UnXthzZhMCotdBqBH/uPYWda1dRtTNLVV09a8+M8Xj7/5Ld8juCusFxdY0VE6XKn282F0GGemhb+nueeSqF3d7GUDSLYQtCmlaY5LR2J7OPm0H7aNPNnnDF6UtKiRXFwQRFOgcSWEIjb3gIXxAJFb43P39qjaoiKxR7SblFCtiriurSltkTDvZoCEXoSCVIVeXJuTaelJhCoyEUQRiCvlQayxNUeRYBXyeveeTwSLo2WxJDzI5W05NNkfdcaoMhknaOsBkg7zjYtocnJZoAI6ijBTUyrsPCWB0d6SGyul+49clCc3KVFaS1rhrjfMH8N0K8vHo7uq4xo7Wa+GhEWSZr09K85znOB5OxU/iKH4UQZJM5UsMZerb2goBgJECsvoruLb0IIdAMreDhdstE8thisqz8qOsa+azNVd9QwlhRQFWQFRPy8SUnkbEdUnkbX0pSeZuM7fDxJSeVjikfCS2EwNCiGCLE9sRPgcKY5/ImvZzbicBA4lYcv7b/39g0/B18P4dBFboIkPM7aYxFeXHT/0EGT6ClIUcoKKipGiBkgSYsHNcmw5u4OR3Nkkh/1H0sd/3zfR/f1Uj2G2SHLayGLH2G4NHXj+GNly1Suk37B01WnJjjtW076E4kSNs2/bkMW4YH+drKJ0q+31f6v0d7OskrQ728OdJJVuroIsTJp7zO5nwSMwtaBBI1MNCkk2gy2NzTt1eDBMop9zoOp7IMp7IkbRsv5TE8Gr4fDpp09B+eFSCF4nCmq3uEcMiq2DZZRXVFVzvXPPGbCXsBxg72qA+GcKWPb0jikSBmQGfEy3PlotOoG7GIu4GSOE6Yo7YHR3BMTQOGrvH/nbiUpnAU1/exPZ+RfBYPWNTcxEktLQQCJrb0eXOoj4hh8aFjjydcHyRv+NjCx9F9dEtnTk0NEdMiodnc8o/v51+/8gGaGmMYho6UknQmTy7n8MELF0/3S73XrHlqLasff5XNq7ex4eXNjAwkgUKWcjaV46o7riA5UIh7C0YCtB03Ezs7Wk3XChFuhRi4Xecsn+ZXwWhRxQyaBMKBcYNB9nbdtyy/g2uWfJlblt/BmqfW7vO5FIceVUFWTMjpc2Zxw7nvqkixuG5ZZYpFxt1JQKuveJwnHfqzz/Nsx3Lybj++5hA2W0f35QANXQQrjk+5mzBEGF0LIfGQEgJ6Pb7fy8VLXmTn4GtkbZ26qgGCpoU5mvTg5Fx8z8AeCRFqLOQuIwtvSQJIX2C7Oq6vsSNRw8bHL6DzxD7yAY9cf0FgmjkYifrMnbOD05u3URdMM2xX8VzXIjoS85gTq+G+N1dhWS5dmQ1kvBBZx0VqEjeYxbcD1DWlSS5wycU1fAvwQfNBmjBi+ixpmLFHr/nLG3bw86fW0NE/woz6OEtPmsmDybUkM3m6+hJIveBFjPQabE0NMrelFlPXmVF/eFaAFIo95dWVW3j4J88f1IE9Lc3xUhZ7kYkqqmMrxGN7C8amWozYeUK6Ucgx9z1CpsmMQIiX+jo42W/gBXpAQMpwkKPly5i0SrFxv2/fWPDWApauk/c9POmBhIxr40qf+dV1tESqSDs2969/BU+TmLqG58tCo9+oGCxvHjy5LDnoQA4OKUWebemled7+R54VrRWaoSM0HyfvsuWVbVghC8d2icYLleSapmrqZ9bR297H9nUd5NO5QgPiqLVCArqhF3KSRaHyHI6HCEYDmAGT7i27BoAEQhaa0Gid38yWV7dzy/I79vr5lOcj1zZXM9w7wj1fvE9ZNY5glEBWTMrpc2ZNGOtWRBdhhvOvInHRRRBDj5NzOtG0AAGtHqm7pN1CpSVktCCEge/nCVlzS+fIOO0w6qtzZRKJjwRcN40uQtQGjyHc1E3GHcCTGcDA8yVCFHq9830xpC5Zed8xnPSRDQRjTuFcrobrG+i6TyZj8cQbx7EtliIcywIa7kyBZkqyUtIWG+C85rXkXYOEHSBsZDl/zgs8stUnYr6T9uQID7avYLYVQ8gkEgMxKsb9QJqdmSh+UOAXlgRa4QOeIJwXvNTXwV8zdTD/yxt2cOd/P03IMkv+4t//73qW/59F3PvaSjxDEsVCdPiEsiZSk+zoHaalLsZHl5005bkVisOZV1du4Xu3/ZZg2KKmPsrwQIrv3fbbaR/Y88ELF3P39wrNduU562MrquUVYqD08b43V7G0ZTazq+IVuchZ18HUDaJWgJnRODtTI+xMjdCZSfL3Z5zJwCMpOpvzuAEfw9MI5AVzZhSmFkZMi5d7OzimpoGjqgvbBnMZtiWG2ZwYJGyYzInVMCMaK61l0/AAEjihrpmNw/3oQkMC2xLDzIjGSs2DUBDJB7IhbzpEYdFaMWN+M9vXdSA9D8d2cB2XQChAVV0V93zxPlzXZdsbO5Cej+9LpO+Xmho9zy9k4I9aLGYdM4Pqhhi5VJ6/uPxdvP7nNxnuHcG1PYzR0dJNcxrJpfOM9CYY7h3Z6+czmSXkobt/pwTyEYoSyIp9YiC7kpzbh4+NwMCTNnlnGxoGYWM2QghCRgsAtjeM5ptEjfnk/T40DKSUeDKNRw6NIB4ZCgFHGuACEk2YOHKErNuJQBu1Z0g8MhgySq6vBowcRsRhwTk76X2jgc3bmli4uIO6OcMA7Oyp4/erFrO1qxk/7JMXGrrhoRmFwHhfCJbWbcXxdWzfIGw41FgZLN3ho/Of4f4XBe07Gkkl0+xobuOclleQmiTvGwQ0F1N3eKrjaEBDaD7CkyBFIYpIQjaq8Wzn9glH0ZZXjAcSGeLhANkawVq9n0zQIeDomGs209Yfoy4WRgjBSCxHp5Mgm7dB1/jCh97FqQtnjas+q3QLxaFmT6vCD//keYJhi3C08M5S8ePDP3l+WgXynlZUd5d7PDbVwtR0cp5LYyhcEqy6pqMJwc8G3uCSC05hw1Md/M7bDmGN2TNqiMdCACVLVcQsWD8Gcxk6Ugkc30MTgpBh0hKpqliL7RUsazXBEAuq69mZGiHj2EhN2+MEnX3lQIrCYiX6xd+uIhwPU1UTQdMFqUSuNPmu7fhZxOuqSCcybH6lCzfvInSB0ARS7gpG1vVCHwiAFbJID6eZcVQzAK//+U2+8uAXJ5yI19feR8Ps+n16Pt1beqltrq7YFo6Gxo2qVhw5KIGs2CeKDXiWjJNzO0ftExJBgIC+K0c4qDcjhMFZMx4ECsK6PA4ubh5Pwl4HFHsl5K7PfEnW6UAIHaSGJrzRwR/gSR2nBqLhDMlkkGBdjlhriqaT+nj2tyfxxyffh5aTtM3t5p3Hr+OiM16k147wbM98+o1dA00Aqs0MKTdA2LBpCCTxETi+Rkh3uPj4l3iyawnb0lWsG4oDp7C4biMxI8WwHebZ7uPYONyMARgIPA18KcEU6LKQdWoIbVzc29iK8ZvJPrZUj+CaPjoaYWngGpKVXien1beSSThEQhbxaJB4NEg6a1MbC5fE8djq853//XRJPCsUB5u9qQp37xiipr7yZ3JvBvbsD3tSUR1bIYZK60Ix1eK+N1fRnhxhQXU9PZkkA7nsaItw4Z4wL16Dqek8Z3fwn//4fs7vaufGp39Pe+8QXnsfetCgqibIsTWNdKUSdGWSJG0bTQgsTcMyTAZyGVzfJ++5ZF2HkGECAkMr3BdrgiFqgqHSZL/pFMdw4ERhhViNh8gkMgz3jhAMBwqjn9llnSteI5fKEYwGcW0Hz61MrNB0Dd3QC3YKyyCfscet7aRli7jqjisqJuLFG2I0zqy0De7p82me18jODZ2M9CXIZWyCYYt4Q4yZC1v36rVQHD4ogazYJ4r+Y0MTJUE8nHsdW6Z5vauHnOMSNA1aqw3qwzNLwnhgaCM96yVv/rIZKz+D937hDGTT62gEkLglD7Lr6TjYmIaLpQXxcTFEhJA5g8H0BqSWIhbV8DxBNJLD9XVs18DQPc563xq2PFqHr8MFZ7yE7RgkUkGqYjkunvcKf+w/lvZc3egkJsGQHSZq5Km2MvhSINHQhUfeN/Cl4PRT1tH1x7NxlmbYOBSny1mG7TnYnktiOIAuBFWhALb0yBgukuLNWqALwZxYDYamcd+bqzCTgp8/tYYX1rWjaxqzG6sZ0LOkZno4RqHLUGqQFDaaFEgDnpc7iPQbVDsBZjbEsQyDrO2UrBU/f2oNIcsspVqodAvFoWZvqsLNs2oYHkiVjoHdD+w5mJ7l3eUeQ2WqBRR8y1f+6ddICpFtRUvE1sQQr/Z3c80Tv+FMawbVr0gyzeAEBVYeql+RHH9OHT/pfx3X99BEYeRRzvOYEY2Tcx060gkiuomQMJjK4AlJwNfoGkrQVF014fqmi/Ipd0UyqSzNcxv36jzlleiq2iqGRhsls6nRQR8CrGiQnm29xOuqyKSyCF1DNzR0I0A2mUVoGt7ooKXRFj0yySy6phGqCk24trET8W5ZfkfF8xkZSNKxsQvf9bll+R1T+pGPf+cxvPjbVeimjhkwyKXzpIe7Oe9T5+zVa6E4fFACWbFbiiOnXfkqp857jZm1WQwtgdTdko0CwJMxcnYKSQbLCCLJ0DGc4/lNrcxp/Ad8x8TuyBOxPE789Ahv/sTkR5/t5YP3z0OGOnGkIJ23cJwQkeAIri+w80Aghy4kng/DuXVI4WA7JkKAobkIDTxbw9c0pBRYQYePX/QnbN8gbQfI5kNoAnKOBT68o2YbW7c1IqSGZ0ie75vH+TNex9JcXF9Dw0MAw5kQmubR1tbNJz70GAOZKM9n2uiVJkE9SI1oIhIpTKUKYRJw3+Qdc9ZSHUowkI3y7M5j8eVx1ARD+FLyZl8fd64oVHr9QsQGW7oGyFT5BHUdWxv942BUYPtCInxwhMSeK8nsdNnUMcBxc5oqqsMd/SPUlf2CgqnTLZQdQzHd7ElVuChyt7zZxchQmqpYiHzOIZux0XWNj332/0x47oPtWR5bIZ5dFa+If5zsMWe1tpUqz4O5DJuGB7FdF9vz+N2mDfxebmBuIMyyoV0VxrSX548bNzGntYZNwwP40sfQdExDZ8TOISkMIDGERiKXx5CCiG+gu4L+HQnyvsvRjQ27Xd++MrYh7/h3HsPj9z8NULIp7MuAjWIleqQ/Qfe2Saq10i+IzkSGXCpP23Ez6drUg2MXkkBgV1JFPleYGCgo+JHtnE1Pez+GoU+5touvPY97vngfAI7tsv21HUgBcxbN2q0f+fU/v0nrghZG+kbIZ2yCkQDxhjiv//lNPnj9+/bq9VAcHiiBrJiS4sjptoYO3nHUk9iuwdY+kzkNYRxZaMAL6s1k3S6ybheeb1EVGsbzDAZSjfzh9ZN458I3EARIJH38mIWZk+h5hznnbWd4wxJe/eFsTrk2TSrbh6k7GELiuAHS+XpMbYS8SKL7HtJz0QIeaBJN83E9HdMo2M5MwyvEJZkuSAiYLiEcqgJZNE0yNBRFT0nyUYNqK4MQEmkUsuC2JRp5TBzPh2e9TEh3yPsGw+kQSI2myAiu1EkIi7jM8+GaN3iDo3mlv5ZZVXGuOKVQpXlk638zL7aSvG2QyYWptWwuO/YV1g610J+rZsvAADsTCXbEhgm4BtGoTjBjIIRGUuaI6wE0bDy98vWXGhhSw9IMxCyd4+L11FaFKwTtjPo4g4lMqXIMkMk5E6ZbKDuG4mCwu6pwucid0VaP53n0dA5jmjrhqiA1dVH+9NBqjjq2tSR6i4J69XOb0AyNmW31CE0cFM/y2ArxnlCsPCfsPFtGBrD9wjtEmiyIXFv6bG7NMJBrB0sQdU3mJ6oY8nMcFWlkMJfB9jwMrdB0l3UdPCmJWQECwwLLCaDrBftBzvB4R3sDNbkwt3z0/dPyGoxtyNsokjzauwrjqhkYfTliL6UIVEUJzG3lv/+8EVlbtccNgcVKdM/2PvzR1AkoJE9ouoaUEsf2CIYNqhvjJZF711X30Lm5pzBeWtMIBC18KbGzdikhRDM0fF+SHEhyw4+vm9JLXG67WP34a5ghkxnzm4nXx0rHTOZH7t7SS+PMOppm77JoSF8qD/IRjMpBfptRPgmuOMluKoojp4+b+TKebyJlCF3T6B4OEtZnYXvDpJzt5LwekpkqktkWMvkaPD/IM+uPo2NgFtXhEVwvAJ6PBqQDAi9vEKxLEY6GGOwaRojC1ChN+BhGlqCVIhbcweCgSf9wBM82C6OkkOQdE9fXKaZfCCSaJrGMQnMfovDB8zWEhKZwgnAwR9TIMbeqn7pAig+3vMTsUD+6AA+N13fM4gerzibhBNHwaYgkaakaQgjJQCaC1AV+QqOxppEPHrWDRy76BP/5nveXfnF+8KgdHFc7i/l1M4mGgqR9k6QjmBV5nk39/bSnR5C2RNqSHC79TXkSgcJNXMtLbN8DxK5BJ+WRnaLgb85ozoSV4Y8uO4ms7ZDO2oV806xdYcEop9yOIURh2EjIMvn5U2v25z8rhaKCCz92BrmMTSaVQ/qSTCpXGOP+scIY93ILhtAEds4lFLaIxEIsPH4mDS3VBMMWD//keWCXoB4eSBUi0Dyf7Zt6SA5ngIPnWd4blrbM5oPzjqMzncAZFcdQ+KO3aMKSOiQsh6Cnk9M8VlUPEDQM0o7NjGgMT0pc38f1PExNx9A06kIR0hmbbM4mkcySyOUJZDXCIYuX+zs445v/l2Puup0Tb/0Gl930g91OBtxTym0QPQ2Cte8I4UR1GMwRXtDA1nOqMRbXM2N2/R5PJSxy8bXnkUvlySRz+L5ECDE6irtwT5S+RDc0Lv/qRwC454v38dDdv+PCv30vtc3VBEKFQSGNcxqwswW/MaIgjpESTdMIhK09ahw8adkivvLgF2mZ18Sxpy+oEMdT+ZGb5zWSGc2nL7IvdhPF4YMSyG8jipPgbG+gYvLdVCK5OHI6EhgqiFxA1zRyTsFeETDqiQeOJmYtwPPr8HyJ6wVxfZNT5rwCAhLZOIaeR9M18CWeJtADLrmBKJlUlmM+1E1QrydvNyBH3WNSgq77tLYM0libZLgjxNDGRtK5INIX+L6Gpvn0JXZVSQXFmB9B3jVwPH10xLWkITZCc+MIlm4jcj6L4l1cMWcFJ5o7SPZFcLOFbOXi40GgCYlEEjBdWsPDNC0cICV30JccH/6ecXeSsDXWDfaWOtHTro6h99ORTqDlwHA0NASaL8CTZBtcdCGI9BnkbAcpJYa765wC0KUoxN4hCfsmmZxDOGDy9997hPd/5fuc9w/f419+8jjhgAkCBhIZamPhSSvCHf0jhINjRoirYSOKA8yJp83jMze8j+q66IRj3Lt3DBEqyx/O5xwMUyef3TV1slz0lgvqQNBECIGua3R3FPbvzrN8qPh9+8ZCikXRAjC63RM+6JR6DvKaR8KwSVkO+bBPbyaFqekcFa9BCEHe91hQXc/nTjoTJ++S0R1SUZ9UrSQT9clvy/Fy/042zUozbGcx8uBYkpfjfdz4w0cOiEju3tJLOFrw8q6fJzBcCEodO2OT6M9gorGzIVv4wzscIBg0+dXDqyY819iBGgBX3XFFKeNY1zVC0SC6oeF7Et3QmbGghcfvf7oigu3x+5/mI393EbMWtjJjQQvJwWTpRdZ1DU3TEJqGazulRr3dUVxb15Zu1r24kZH+RGnfVIK3KPLTiQzSlyUryMXX7p3dRHH4oCwWbyPKJ98BGCIKfmF7XWjinN7iyOl0voaAmcL1gni+T9A08GSasDGz1LAXD3lsGxgqJDdogppIAtfz2d5zBi3xPxGt0hgekYS0PHrAoeuFJo77zHPUHj9IyklRE83heQUfrhytpnqehiE8Yq0ZBjaFyCajNNYOI6SL7Rp4vsDxCvFvpu4jhCTn6njoCB9s10fXJCHLxUnraJqODApsTaALnwsWrKEjXcvm/iaWta1nJB9m0NYAQWtgiIDuUB9KYedNPFfHNB0ydh9fves2Ore0lKKhwo0z2TC0DtsvNIjoQmDpHkO5KlxfEnQ10AS+P5rS4YNt+sSDAWZsstGSQV5clMPRfIw86EEd09DI5B2EBA+flkyM/kQapCSRzjGQLFQr0tk8AdMg77jUxyN09I+UKsJjRfLe2DEUiv3hxNPmTWp5KFowXMejp2OIXNZB5GzCkYktGeWe5qYZNbRv7kXTtFKVOpexufC6M6b/Se0FK7raWdPfTUA3COkmSd/eNd247B0iX4ch3UaXgohl4Qq/5J91peSMllkVnuJHH3+NreFCQVpzQc9DenZhiIhwQLjgI/F1cEKwbmGK6555hH+vv2C/fMnlDXmpCARz4LkegbBFNu8QMHVS+q4/cCabSjhVdvINP76O/7jyuwx0DWNYOrqp49kedTNqiMQro9eKH1//85slW8SGlzajaRq+71Mee+F7PoFIYNxaplrbzGNmsP21HWx9fQdzFs0qNN6N+qsnG44yNhXj4pv3b2iK4tCiKshvI8aOfgbQRYSMu3PSxxRHTr+x81R0zUGILJ7v01pt4MosbbFLCRszGcoN0pNIETAMDE3D0GwGUzGiAZPB1Dxe234eOb+K6hoXIxFm7XOt1PxlJ/5CnZxrYfsZIIOu+4WJeIDvC7DBd8EIeOgBm2zGYnAkDGhIV6MmmqY/GWVLdwu9A9Wjz2nUe6YVpnb0pmLYORMHA1cX+EIDXxQa8oTPe9rWIXWoD6ewMyZ+TgcPRvJhDF0ikEhXIHSJFIKhkTDBU5/hyRP7eKBhI5944pdsaD8FV2YI6g6aAEvLY2Dzpy0LQCs03glA07TCjVsDI6/RNgLNZpA5bhXv2hIjZhtUZ3RiSYGpGximTrUIMXM4yvxwHfXxCPXxKEOpLLomCJg6uq7TM5yibzjFls6BCm/xyxt2VHw/98aOoVBMFxd+7AwG+xJs29iDY3tYlo7vSfI5h8RQepwlo3lWDdlMHoBYTYTZRzWi6QJNF+Oq04cL/7H6WTzpk7Lz5H0XvXzscTHNUgBaoYrs6pK072BpBg2hCHWhcIWVq0i7nySU1AgPCoIjYGQKojhfDXYUsvWQaYR8LfgW+AYMyML0vxVd7bzyWjtfufU3fOa6H/GVW3+z1zaIdCJDNC3JCw/f9Wme00gwYJKXHlF317tTE00lhEqrhtAEkViYYDRQ8vZ+/t6rWbB4LtIvvEYLlszjc/dcRS6VL1WwixQtD0VbxOnnLyZaEyEUDaFphal6AghVhZh3YtuUz2/NU2u55cPfYPOabbzx3AY6NnTR0FaPFTTZub6T6sY4V91xBVCweIwdJrLmqbWldfznyq/zlQe/qMTxEY6qIL+NCBszsb2BQuV4lGIVeDLKR07/+U2XU+e9xtyGLPXhVtpil5Yqz+v7biRgGkgZJBp0MTTBpu6zaKutoTYSZn1XE+ncJ/j4kpOgFnL1nwcrgiFDJLMCIfoIjN5bNSRSgpfT0ITEzRsMtldhJwLEYhn6E9U8/MxSzjzhTSKRLJopmVk3gGW4uK7AMjxM3cOTgpF0BC2t0dtbQ/PcARxfK7ytOXodx9OpDyexhmBwJEoklCPnWYghjXzewDtqBE0IdMNHehZ9Q2GGDIvqWBJXFnyFybDL11b3MafpHZzVuo5aK0FvJsKz3YvoMWPU1CfxYxqZpIXjm4U/SyXUpC28zgx2fYA3SJJJ+lS1a+QbJVnX5Z0t88Z1o3/s335CXSxMznYxjcLft7ouSKTzhAMmrueXvMUwPurt1IWz+MKH3qVSLBSHlBNPm0ddY5xkIofn+ISiQRpbqxkeTLNjax+nnDGfC6/bFd124cfO4Hu3/RYoWC8MU6expXpahPGKrvYJEysm2z4R3399JSt7OmA09NH3JZoQGBSqwrJwA6rsNQA8KUnm8ry5uQvDMnilvp2TT6i89lDMQQz7BDUN3xf4wsfXAQN8QcG6MaaHIRcs+Ji/9cKfifzJJhg0qa2JlLzC137mPbttqCuvkLasHGTTOXEa5tQSrY0S0yQjPUPM7AshkZNOJYTdZyeftGwRdz55S2lfsVrbtaWbnva+iqa5sZaHi689jy1rtjHQNUwwEij8HrE9apurp7Q6rHlqLf9x5XdJDqULwhpJeiSNk3eYc/xsPNfjKw9+EShEwamJeW8PlEB+G9EWu5R1g7eDX6gcezKNK7MsiF065eN2jZy+YML9daHTeGbduzll7hoigSHS+Rre7FlKd1cj27p7qHldcOqsGi782HGcOGcW1/3yEU5bOEI2EyKfzRQqR5EYDbUJAlahSuTmC95b3ZCkRoI8/dgp9G5oBN8jU2+RaDX44DnPEQnkCAUdPF/geQJjNAUin7HQDZ+onufZPx9D744aPnjdE+iaxPMK/mJNSEZyQYa8MHY1/KnnGD604KXC4yMmZpWD5xskhqJ4+Rpc12c45BAwbQayUcRop43vQ1q6vNFTz9q+s8EH03KI1OTQhI+0QZM+0focqRFwbBMk9EdyrD7ewO52Cs8VCCQ1AsOwNFDHXZ9//7jXumiRCFoGmWgaf3YaL+RCSsPrqCKc2vUOwWTe4lMXzlKCWHHIyaZtjjlhVimaC6BpRi0d2/sB+N5tj1ZkHH/mhvcVso93DtE8s6ZCQB8oVnS187WVTxA2TBpCEfqzhcrrB+cdx6+2vDFu+9hJdSu62v9/9t48Tq6rPvP+nnO32qv3VbtkWZLxbhkbMIwJBkIwmGVCJmaZSWIyE08MQzbmTcIkJCSETDYHMu/ACwkEJyFjPBCbJSxxbAK25V22ZVuWtbd67+ra73bOef+4VdXdUmuxsQjB9ejTn1JV3bp1azvnub/z/J6HP3n4X7h/ZgKNQQJW0h2BMgaLJMyjHPhIIZKT7OUwIDX4KUMuMLz3m/8X+zGHmokYy+QZzRU45JWoFUNkRSNVcrKvsiQBpDYnkG40YAnmgyYz5SovTw2Sbem/25e33f7QGTlOLPcNXk7aNwz08tNjL2HvXROnTCWEk3snp7IeH/7JP1ohWwDOSPKw/Pj+2yf/M5/9H1/gwO6kMn7OZZt492/95CnJ65c/8XWqpXrLFQSkSOw74zDm2LNTXPjvlh77XMNRTibH6OKHH12C/CJCf3on2/t+ZUWS3TnLqsDL0fY+Ploqs6a3yDsvu5DLN6xd1RN5MLOJwfwG7nribeS8pHJZWWyw/8A0aSS9A8UVXqVHS2W2VfOgFwmli7EkUeghyikGshJkhO0p4sgwdaife++4gMWJNQRDhvKwJErBVVsfoy9fw7ISOYVtGezW/1tpoxx5Zhg3FbF28wwP3bmdf35qO/9u21M4liJUFmWVQjuS+2Y3klIx+6dG+KK4jH83+hQD6RoLzRxf3XcBL+t7FhVX8X0HtzfCtWPuenZbR0/YbrQRYaKe0Dak8mFyHEpgNQVxziC0IZ0JiZtO0ogoYWI0pn/KwdJghKCBxtXQWLPS1xgSi7ZStcGeQ9PQHxBvrdB6CkyPQvWWyE57nRb5rra4ix9mHG8FV11scHDfFH4jolppMr6u/wSP49MR4udS5V0Nn3vqITK200nNa19+8on7WZfvOeH2zz31UGf/bXI9UaskzgktYmyJFkk2STV5OJOjEvgos+RssRxaGIwwhE4iESs3GriezUS9StpxGPPyPOPPE2bA9cE4YGxwyxAVE2eMFWi54tSjkFw9cbtYjpNphU+HVa3vXr56L8tyLPcabnsnL0yWEucKoG+kh6N7J/mtt3yMOIpx0x7jW0boGSggLljHxDOTHH36GBe/5vxVNb7HV6DPBFP7Z4jDGC/j0awn4SRCgIo1QWNlo91zCUc5ld66S5J/+NElyC8y9Kd3nrQhr42293HGdRjIZVioN/joN+/movERbn/8adb0H+GNF9+L1h4HZj3E4DEu2fQ0T0xexOMHR5JGk8UA4wi21TIneJWueUWROx/YzGtetgs3iggih2K2xmChRmm2QLA4gOWEWG7E926/gGNPDjB46VFe8fp99OWrBIHF+PACSfD0iTOMMZDOhmzcMQGmFRv9U1M8PLceez5me3GStBVSidL8y/wWDlUGSI/5qEXYVxrh2fkRnEqyI+MIapkeXjqwl+JAjelmjtsPbOPZxZHO8znpiHQuxLI1KpI0q26ipQ5F0jCTItEZGrBs3bIwMonGzoZs2iEMYrQySEvg5B3qrUSoNpb7F58zPsDekX1oDcpNmnlsJMooZgfn6J/pIdNwutriLn6osVw2EUeKg3unCIOYVMoBbTj87Azrtwx37N7OhByvVv09vsp7KhyulhlMr+zTyDoui4HP9j73hNsPV5eIZZtcx1phCYluabmUWRqj8rbLeK7A0UqZWry6q4IW4GhBWtk4QlI2ISKIibVmT2OaTNWikLKpuQqdNth1SJdF0nws9An7s2RrjJCSi/QgjWbYqRzDybXCZwurNbOJVsU2W8hQnq8yc3AWgyEMYhzP4dCTE6zfDsWBAoXeHAvTix3JwwuBkU1DTB+exWhDJpcmaAaoWGPZFufu3LKCzK5G8E8WjrJcbw1dOca/NXQJchcnoO193K4G5zyX6YUyf/PAbqSCl+58DD+0CJRFyoZD8wFSaF617UkOzY6x0GiibUPRrOwBTWc8Djw9RdoO2J0eofbA5Vy17Un6c1UyXshsuUC1mWWoR+FmyjhezBt+9rs88LVtnPtjh0Aa8pkm2f4AKQ1ai06fy3LI1tMKAUYYXEvxX17+TRaiHLura/jO4lYsoZHSMF/PYUILYxvSPT4pnRBd0yMJ5l3ius2jc/3M/P3Lkdqw8BKX8nYwrbnSSUXkenyMBq1AWppcn49WYBmDdlumzIaOK4cRhs5bY8DPa/Juqn2VejNkvtLg+t+/paMRPj5OOtUjUEoihKCQSR4bxjG+CnjazPNjhU1dbXEXP3A8lwjo5bKJh+/Zh5tyMICbsjvVxKmJElu2j52Rx/HJqr833/ddvrrnodMu/QOsyxdXpN9N1CrUoqShdbJe7URGQ1KRXZdfIpZtcp22HWKtaaplno0tjqx8RbnSxPLNibNv4ueYaJMDMEoTaJUkhdoGKQWR1oShwXNtRoMMLzlaYP/BOcqbNbPbNMIcV5QWiaNFStq8/8KXUcjCX973PZRSpFIufb1ZLEuuqhU+mzg+4vkXLvu1jmxh+uAM0pZIS7as2QSWJZg+NEtxoHBKq7VTyRlOdd9y7bLjWngZr+Oe8e7f+skTjv1M3Sqeqxyjix8udAlyFyfgaKnMQG5p+aiy2GBqoYZxwBaCvlyVSiONtAyRUgRxTM7zKGYqaCCfcqnVAyq25u6+Gn2RxUuqaeShBovzNVQlhcjB/ulR9s+MgoEP/MSXqdZSZLMBhb4KUWiII4mXibjyusepRR6FTBNLJsQWSEiyWeHmswJCJORZm4Q0F12fi4tHeLi2FhScm5/m6uJTHKv1cN/cBg7rfuKwRXRtTXrUpznhYlUEIoxRtiTTbCJyBgoqccGwNEaDafk2GZM8oRBgUiCUwSiBNAYsaNbchBy3ZjErhoWRgGBREWUNoa0QAfTEafpzS24UdT9k3VBP57V5kUfFDhF66STEsgRFK0dqTZGPvWl1vXgXXZwtnC4C+mTk+YKdm/jFt32c3oEc+/ZMEEcKy04S1IJmdEYex7vv38+uPQeQ1ZhUymN4TS+FngyqqXhycpb+xZEzakrrpN8FPkfrFYRJ+Gp/JsfBSolGFNKMI+pxiC0t3r/uZZ3Htsn1eK7AY3NT7byiFYy1YSkeOXYM7cIJZ/cahAZ3ETwlqDshMhZkbJumo9EYhALtQFPHrIvyPL6pQnmzoSEUVizQztJ42H5a17K4YGAUtwz/+E97GBrMs1hu0myGTM9U+E/Xv+yME+/OFpbLFoJ6iJOyUbEmU0ijY42wBH49XPIWXqVaeyo5A7DivqN7j/Fbb/lDioMFNl24njff+PrnpF0+nuCfyetqoxse8m8HXYLcxQlY01vk4HyJhUaTZhQRN2Jim051YqGWJ5tqEkYuioQMek7EbDWLJZN0OyOTJTNlYM5VfKe3xsBUkx2jRZ4ZMFhaooxm8/AUL9/xJP35Kj3ZGhYGoy2M0kjLEPo2biqm16sh2vteRoqlOHGeWQ7T2gZAmoTUnp89iiM1sbYIsMg6Pj++7nG+NnUezwYjScFXA0bjDYakv+Mioya1nQXUy0K03dqv1NiuIo7kCmNTY0BaUCunyHghjqshtmksuETa6hyYFYIbWSjXUBmKsJsCIrBTFpOpBlZcYqHgUyPENA0ylqx1korVSGWYhf4aRhqMMejWv7TuYe2yqlYXXfygsDzMAxLZxMzkIh++8a/ZeO4IpbkqrudQmq9x9MAsD9+zj+tv/DHe8u5XdPTIbY9jAGMMliNP63HcJubeTkOUtojCmMP7plm3ZZgjc4vklHPGTWlXjK7jN3ZezfvvvgNtNDnHo+imKIc+kYo5UivjWhYFN0W/l+a2/XvY0T8MwHyzwe65KTK20447WmKpbVs3CXGGpFKsSTpzWdrGaULvk+C5gmPng3IM0k7kVkokVecgb7AbsC9fIa1s7ECgcxBLg6XBMhKNJm7lJBUcD4Phjx//Huf25lkvigwPJWNEvRGwe88Eb7/u9Nrhs4VH73qC8myFvfc/i5f1EBaEfgRCsH77OPN9Dnv7ICy4NLw0/+mVO1clp6eSMwCd+8pzFaYPzYExNKqNFUT6uWqXT4fnIsfo4ocPXR/kLk7ApWtGObSwSDOMcKQkMhotwNOJZOF7T+3As2NcJ0QIQ0/GIETAd/eehyUEfhQti0tuEThtmNnqccQ0mQl9RFOzaXSKN710F7lUk+nFAo6tSHkhCIWwkvjo2nyW0LexrNZcIwQte8wT1MexFigjUG2C20J7W0saLKnodRrExkIKRY/VZDRTpsdp8MbR3Vw6cpgdQ5MUcg20JZCOQVmG2oY80UVNlANIg+1obC+ZuCx7pe4vae6QRKFDcyoH00OstzdTVEWEBhlBT+gx4GbxHJtYJu+vZZLErZRlo43haWeBQCjSwsbYhqdTCxyJykniXilFcWIAWzqEJsTGpqiHMLHHu7f9YJdLu+gCVqbjVUp1Dj87g1bJb//wszPMTpY5dngeFSm8tIPRhls+/m1237+/E01tOxZrNw0m8dOhYsOW4dNaubWJ+UsWcsQWKBeEJThybJ5mHLGtvvKE8XRNaVeMrmMgneWlI+u4ZqTEL22+lc9c8jfcfPG32dl7DFdarM0VGc8XydgOf/Lwd/n1u/+RA4fnccqGeiNAGd0e/jqXwFLGtNX608vusyFdgtEoy6aNQ3iu3VoBS95DY8CpQ2oB4jT4VkygYgI/xtbJcpkCQpGQ4/bz1cOISCsqhNw3PsdXnP08MHuUSrX5vBv0Xii0q77GGDZemPgUB40QrTTD6wfw1xfYuz2H7E2zfdtahi9Yw60zh9l18MgJ+1qe9NdGW86w/L7pQ7NYlsRNOYTNaIUP8wuNthyjZ6jIwvRix0u5qz/+t4FuBbmLE/Dg0UnW9xZZaDbxoxhXCExoUA5klOTg9Cj/8MBLecW2PWwfh6yzhtseWMeh2QJ+HBLrZBbYNDjJVefuoT9fZaGS5+6ntrE/GkE7SbX1qq1PEkY2QewigNmSxVjfLNKOUH6Kxak0de2BDMhBEthBklRnWo0vBqhGKSBpfLNETMaJEW0dcus1aUAbQd4OQAgsEVO0m4hkbwB4bkRR1oitAlt659g/2085SBO8JcZRIVZBo2KwHDqVbEjkG9LW6DhJ/xMSmhUXN5C4KYtAKwrZFJYrmZtq4BqJZyU/PUsKtJUsrUohCLWm2gwwueS1OUiU0hS8FD3FNDPlBukZm/GBIv/lVdcR5U2na39tq2vfqQp+9Y47uj7HXfxAsdyVYnqihGUlP8JUxiVohsRKIbXAyiQ6ese1CPyY22+5h1//0+s7euT9T03iuDb5YoZiX/ZUTwkspexlqoIdT/g8OdigljV4M4YL5DA534ZlpjCNZohY5/ALd37ppG4X6/JFBqw9vGP06zQim/kwQ5/T5De27+JP93k8XnXoTaXJOi67Jo/gLBo8bPKWh2poalaInzGsEAULEKo1brRipoEVZ/v1URhtFHksX8aNJF7k4vsRftEgLIhdsOogrGQ/DSemmHLwtGDBhB2PdZbtuhFEPDY1lTQLCog9w5HNAbVHj9F3yGXzpjNb7n/kscPcdvuZabnPFMdXfXsGCtQrDYQQFAcLfLMYkJIua7aOUOzPdx73+QcebVmPLuF0cob2fX4jxHHt5DsZKR67+0ncjMPC1OL39VpOhjOVY3Txw4cuQe7iBBwtlRkp5hnrSRpSKosNDu6bpgnklQXSUH5mjPUb386bt14OwMT0g9zz7D2oZeT4usvuI4htqo002VST63bez5fE5eyfGgUJfcUq1WqKfManJ1fHs2LiyMFyIvxqPw2hsYd8HFcTaokrk3LLckmFNrDQzPHtozsQwI9v2E0tVAxlqp1mPW0gMDYCgyM0C3GWotVozSWmszMJnF88yrfnz0MgGOtbpLbo0evVGU1X8JyYQNkcqxeohMkgLDBoDVIYsAQqlvhlFxouO+UYzWbETK7BbLPOunyRsUqWxVxIhMZG0FARwgIZJpUiS0qU1sQYHJ2QY6UN6/sLHZJ9y89ev+LzWj6xL3e7WJ6o94G3vbJLkrs4q1juShH4EbIVrb5mvJepows0G2ESZNGCVoZ0xu004LWrxJ/66FfoG8yTzngn6JhXw/LY6uazC2y2JMYYhCVxh6qU+pOVnkzapdEMOWbVWFwr6G9yUreLd2+7hLD0GWqRRVO7YKCuXaQQ/NTax/il3aNA0qgXhoosTueEQBuDqJlkbEkt46uKRFrlg2plNYnWSptpkeQ4Bbs3linZPilfYlsWjmPRsGLQiYWblAJiQzrr0AxiPNdpVQDCJcnG8uU1mbzXQiSJe4QgbKhsgfR9EQulOo88dviUZPeRxw7ziU/d+bwCRk6FkzWxLUwv8kd3/jaPfOoWBnIZ5LJGk4zrcLS0sur96F1PsDhT7sg0xraM4LgrfZLbUgcv7dKoNIj8CC+bwknZBI2QyI87aXhddAFdiUUXq2BNb5FGGHWuF3oyDG3oYwCPHc/Am+uDfOI/vInN20a56dY7eOunbuHT9z7E5ZvK3PTa7/Irb/gH3vmKf0ZgCEIXYwRB7BLENldtexKhDCiYr+UpFOoM9ZZxpUbHSXKe0Ra+H5Hpb7SkEoKFIINqDfrHhUQxnpvnus0PcfW6Jwi1RWU2T6Oaotl0CLSFRiIApSVHmn3cMXkBttRJRUcIJEuFlz6nkUgkjCDlxBSdJhsKCzi2ItISRyo2FRYouI2k8txyptBKUprNU1nIohYcrCnYVZvgUTlNba7Kf3A28hdXX8dLs2vZXO/BMxaBUBBCekJgJARphZ9VxNnkhSpf0wxjhnty9OTSJ3TNr4blbhftRL206/CFux59wb4fXXQBcPjZGT7y/lvYff9+YMmVoqc/h5ACYUnWbxkm35NheLw3IWgikVypWKOUptiXXdGAt1zH3LaHbNu8nQxtecaxw/NJlDtJct2a9QMM5NMMWxa9PRkWSnV6ezJ4L83Tn8+Sdz2kEORdj4zt8LmnHgISTfO3/+A7jDXnaIQ2SmukEGRsh4ZyGU2VSVk21TCgEUdkGhJjJ6NSFCuazQhlGVLzMLhHIKLWClGcVH9tBSIikV9IgSUlUiZVYWmgpgOajqJUjJnO+ZR74oTvyqQCDcl+6kEE2uCHEZFIJB1ODeyQjpRDKBICTuK7biT4/UkkdVQE17Ho681y2+0PnfKzvu32h0ilEi23EIJsxiOVck77uNNBvGSI72yQfHOHw/c228zkxIqq7/FzEUAjjFjTuzQOtmUaABtbcdIHdh9CCNGRMyyXOmTyKeJI4aRc3JSDijUIweC6/rMis+ji3y7OegX561//Ou973/tQSvFzP/dzfPCDH1xxfxAEvPvd7+bBBx+kv7+fL3zhC2zYsOFsH1YXp8A7L7uQj37zbiA5W2+EEdKz+cjPXdtZ1jreK9nIx7ls8z1YIkMcFcjkSqSckEhZNPykaSeIHPpz1ZaBPnznqe38zNXfTuQSRiClQVgaFQsGxko8Wx8gI31qsYdsTQDHwxiwJKTtgKwdcHBmEIGkVM8wmKugtQAJ034BRyrunDuXQ80BYm1hC5VUfllq9LOFpk9WWdQ5AmUznK+gtUS1rCdUogpkLFvhqTCDipPoahVLRJRUh4yAaMAgdDIR1lLwu3u+C8A7XnUhf/zFuzkvHiCTcnh8YppZWUcIhRHJiiyGpFpkQ+gonmnMMzFZoT+d4QMXv+KUn93EXJn+wsqQkZMl6nXx4sHZGIdt21o1yKPtWHHzh27j6KE5gqdCdGw6v/NmPSSbT9E/XMC2La69fqkBry2XWI62PeSvvOt/c+DpKZTSuJ5NrpBm07ZRrr3+Sm744E/w4Rv/Gq0NqYzLmvFe8j0ZjDaU5mv80Sev6+zvjf/wWQadE72OD1fLK5w4mosDDOcqTNs2bkri2g4ZK2CiWSDnegykM7x72yV8Zu+/cJ89nUQ6BxHaNmgJvfshMy8Qs4baFoizYNchtw+CIlQuAE9axCZhvQLBpt5emrmQxUaNdhnYJEbnSXNyAM2iwbSupyJJnYhCU1CMbYIgRtYFHgbtQZDhhOhp0RpblAVqzCKTdtl/YJYP/d6XTiqfmJwq09e78j07mX75ZCFTq2337IU9+AdncRoxvmfx0Lhk87zmPa1gjtXmokYYcdMyf/cTZBqDiUyjOFhYUQ1eLnX4j+f+Io2qT9gM8TIua9YPUujLd+3XuliBs1pBVkpx44038rWvfY09e/bwt3/7t+zZs2fFNp/+9Kfp7e1l3759/Lf/9t/4tV/7tbN5SF2cAS7fsJYPXvNK+rIZ5moN+rIZPnjNK1cMcsu9kqUQvOLcPQSRTalqqFV8gtDBaOjL1jqRzJ4TMV/NJz7AFuyfGaXup4iVjXRUUj4xAq0FQmiyImC8WEYYTY/bPOE4DS0rN5N4DqMEKRNjBDRSHrO1PMYIjBHUYo9/nDmPQ80BAI75PUCiS1ZGdFYkIy3ZUZjEthSTjQJpO0YIRdb1KXg+WSdAoPFknLhXJMUHmg0X2arcqGwyCQklkEYgY0FsWXzq4Xu5dOtaPvC2V9JXyPBsc4HaOTG1cxTaAjsUODWJ3RBYis7pq8EQRHHHG/ZUGB8o0vCPq7h0E/Ve1Dhr47BgRYV39/37+cj7b+EX3/ZxPv/n36LZCFCRIgpiYqUwGqQtiMKYxfkaR/bPotTKBteRtb3MTZd55okJHn/gIM88McGRA7PMTZfZt+cYWmmCZkh1scH8TIWjB2Y7so6LX7aFzdtHOee8cfI9CVlazSJuXb5IPVoZ0tFenVlewd616zIKjmY4jlF+jEWdomtYO/QL3Pm2G/iLq6/jitF1/MLrXsGWI1nsSBA5GjsU9DwK6TmB1pr0HAzeC6PfTi7Tc9DzLKw96KKaikgbjAK7apg6sshEvXbC+9w5afaSpE5i8Jrg2g7bJwuse8Rl82QON+vgFRKvN6EFOWVz7mRu6fGmRbgN2A2obRHMzlUpLTYoLTZWyCceeexw5xBGR4o0mivfs9UCRtqFk4V6Y0XI1GpNdZ9/4FEG+vJs2jqO6znQiEhJC++67R0ieyZz0ama806GTRduYM3WUc6/ajtbL918Wn/lLl6cOKsEedeuXWzZsoVNmzbhui4/9VM/xZe//OUV23z5y1/mPe95DwBvf/vb+fa3v91pwOriXw+Xb1jLzW9/I7fdcD03v/2NJ1QAjpbKZFync70/U8UPbLQA7QjmGzmEEHh2hFAazwnxnJjv7N2OtpeI3uRiH3O1HmYqvUg7QlgRrpeEdYzkKgihGUrXcIRCIzj+myFay4dxbDE524vrKFJOCJZGIWmGLlOVAn12g5f2HmB9eg6AO+fORbcq2cmybyKrqCqPrBVyqNpHJUgTa8g4MZLEc1liyNgxsZL0phts75/iwv6jXDh2lP5sban5prUU2nZ/Mxrm7JgP/d6X+L2//Bp3qv3sHShR8vykGmRB6BmUpbGEwNjJa+szaQZNlmzgMpjOdpaBT4Z3vOpCmmFEvZmEG9SbYTdR70WOsz0OpzMeex4+xIf/61/zwN17mZsp8+yTx2jUQkzLqNyyJEJCHOpOg6uKNQeenuLX3v0pfvu/JkvkOy5Zz7FD8wTNENuRBM2Q6YlEo+w4FnGkkFYSIqEiRXmh3iHobalFo+ZjtKFR8xOLuOtXWsS9e9slNOKIahigjelIJd697ZIVThyHD6/nH//xNahagfG4wfmD57F+7A84b/TaFfu76Px1/OZPv55r6mvZ/D2P9Y965ErJ1LraW5hITSD/jGDjN2zyk4LUIrgNQdNtnTAsd71Y5nzhLkJ6FlIlsBuCSMU8fU6dvS/1GX7tEO975Su4YscGesZy2KGg+LAhuq+B9FtyC5mcvDsVsH1oeorpmQpDg/lTyifeeu0l+H5EvREk40ojwPejEwJGji+c5DyXjOvw+QdOlHi155Bif56tl27i/Ku2s/2ijVSPW9c+3Vw0smmIRm1lAeV0ZPfNN74evxZQrzQw2iz5K9/YtV/rYglnVWIxMTHB2rVLX+Y1a9Zw3333nXQb27YpFovMz88zMDCwYrtPfvKTfPKTnwRgdnb2bB52F2eANb1FFuqNTtpeaT5DKtOgqTyMEDT8FPMyQzYdkSv4zNfyfO2R7eyfHV0yMRaw69nzecvOu8ml6kipoeUhbLTGhBZKSjwnxo8tlJZYUnWWC9sN4koLmr7Ht+69CITgygufor+4SKCSdUgBVCOPrB3wuqEn+PrUeRxqDHC02cuQV8XCEGqLxShNrC0a2qUcpluH2S7fLJ1LGgzCwPpsiVhJmrGDZ0dsP+coew6Ms1DKo10wTpKYJ3TShGcJw3fGnqCaD4hiAQ0XFbud9wJApcCqG7QFUkGsFUIIUq59QrTtamhXqL9w16NdF4sugLM3DjeaVQBmpxYpL9TxUg5e2kFFCr8R4qUcmo0QaYlWvPrJj/Hebz/JJ37nyyzO1xhd10d5oU7gR3gphyiMiUNFKu2gtGk13wqU0gR+RDrjsf+pST7/59/i6ME5ojBGSkE665ErpDv65XaTX9vruO3+stzF4tvLnDggIclP7Rmmpz/Hr//p9Sc5+oQkX3T+uk4z28HD86jlSXrLIKVsrTqFZDIe2WciFi8SGJGcKHewfNhpSa/Esl1qzxA4KjmRtjLMNRvctn8PP9W/g+iBMkqlWGjUKakGXhlMGhwj0TqxjVM2DODRU7QZGiywHG35xHLnilTKQQjBQql+UheL40OmYPWmOjhxDoET9cXLcTLpxptvfD1/8t7/l0N7jhKHMbZrk+/N8p7ffseq+4HnloZ3PE6VyNfFjxb+zbhYvPe97+W9730vAJdddtm/8tF0cbw27HsPb+E1Vz+MjkKCyMFzIww2n//Oy9g/PZro3kSiB+7o4Qw8OTHE1TvSpGwfW6pWk15i6OalI4wh0RHHAmkb/NBGCHCdGAHEkeDozCDfuP9iDk0mneUHjo1Cf8A7X//PSDSB7YCAgKTb+xUD+3igpjgc9FNwfAoYLvJCejIL1LXFX05uS5oDJdjC0IhdUlaMFAalJb6yyXgh1WYKLHAchdECHQs2F+ep7i/grzWd6o8RIIsRTsanLAQmAmFrckWfWgV02KrEC8ACLRMCLiNBPQpxHZv1w71n1KQHCUk+FSF+aH4/f3/4Xo41FxhL9/GT667gkv6T+8x20UUby8fhscGNNGo+s5NlLEviuBZCgGXLJPmsJfXRymC0WrWauhzf+OIDrNs8zOBID0NjvVRKdaYnSmhlUEoTBnFii9iyfLCkwEs5zE4tsjBbZWGmgu1YCGz8ZkSzHjC2rr+jk371my9mz0OHOkl+//n6K7ng6pXf++VOHOmMR7MRnDaoZDkuOn8dN95wNb/yyS8xNxwTZsCuJbrj9Bw0B6B+jibOJbcPHI3JHJNk9trMr4tbpXVWBogACJD1RDss4uR6lE54s60ko8M9nWjtTz56PxemeshmPIYGC+x68ACF/ZrSBaCMSTi3LRAWfOwtb+Sriw9RWmx0QlQgkU+kUs4K54pGM6TZDE/pXPFcSO+Z6IvbOL7npS3d+OA1r8SDE+RnZyJHez72a6dK6+uS5B89nFWJxfj4OEeOLGmPjh49yvj4+Em3ieOYcrlMf3//2TysLl4AHK8Nm5pfx5fu30nNT1PINKn5ab704OVJlLRMyLEjBQ4i8V1rl5NMok0+NDfI0bmB1k1JlahdwY1iiW0Zvv7PF7DvyDjz5SJPHF7LZx96Ob/31LX8demlTIyloT9MpHYWqIpHX9QkEPYKQq4QDKYSycaxWh+VoJcrszX6bB+FxhGa9ww/y4WZWYwS+MpONMyRRyVMUYs8MBIhDMLRCGmwY4FUgGXI5H1UAVwiCv11ekeqFPrrZHM+qhVyQksXbTSksyt1fYgkBMBbSLK4sMGxJZYrO8vA3w8emt/Pnz79VUphjX43z+HyPL/83b/lLTf/b371U3fw4N4TtYJd/NvG2RqH41jR05+jpz9HNp9CqyUG7Dh2Iq9o4bRqDQFhEFOar/LYAwfY8/Ah9j89mbgNeFbSANeIEJZAK41WGsuxKPZlmZ0sI6XAcW3slgxDSDDaMH1skUwuhVKaWz7+bRbnaytisNsOHG0sd+Iozdfo6c+dNqjkePgDoF+Rxi7Y9NgedtFm8SIob4HShaBTYAeg0zC1LcaMW2TmJRt3p1j3sIPUdCzhOmiC3Wzpj1vcU7VI9MbeXoqFRIObdVxK2ieTXiKomYxLahZ6dyd9DpELMoCLF/u5YnRdRz4xPVPm6WemeGT3YfYfmKVWD56zc8U7L7uQRhhRC0K0MdSCkEYY8c7LTiS9Z6IvbuNU0o0vf+Lr9I70sOOKrVzwyh3suGIrvSM9Z8WRYnlDoJDirIaMdPGvj7NaQd65cyfPPPMMBw4cYHx8nL/7u7/jb/7mb1Zs86Y3vYnPfvazXHnlldx66628+tWvPqOzvy7+9XH5hrWdwew9/+sWdk1o9s+OnejDRlKtkAgipZdUCyKprs5X8+TSTepRikbgkvES0mgMBKGFEZJYCTasmeOWr1wNw4rUuTXILpFsK6+Q22oEe3NYocEbDihZabK2T6CXtNI5O6SuPBQStOBlbgllIDQ2kbISjbHw+ZnRPdy0d5BK6LE2v5j4HRtBqBPCXI8cLGFQGJRI7JNsyxBISXZjBWlpLsrM8Y6RfYynaxyLMvzdxDk8tDiCtpecMyxrZZMSiWSToM/gSgsD1HXU6Zhf7nn8fPD3h+8lbblk7RTlepPJ6SrGhuZYhYVDvV3P5B9BnK1xeLDVoLU4X8MYQxwrPBykJQjDCCEFqZbMQojTkOTW977Qk6FR86lXmxgNoR8jLcngaJFapUnox3hpt+NisWbjIFGomJ1aJPCjxLKrvUthqJTqPP7AQcIg8WVuSyfal7ffcs8J5LftxHE8zjQo43NPPUR/Pkuvk2ZquowODLbnUj8vJtWUSG2QjsB1bBo6pnaOwPmXmLliTGUcdDvkqOWCY9dbPukp6H8MGlslUcbgGsFIMU/e9nj6mSn8IEKmLHLKodEMOxXhdWv6eGbfNNaCoe9BG9u2yOc8fuV9rwGSqvfrXr2Dv7zleyilSKVc+nqzHD1WYtOGAWCpsny8c8VqsocPXvPKFbfd9KrVXSxg5RxyKpxKuhGfxEv5bDhSnMy3uet+8aOJs0qQbdvm4x//OK973etQSvEzP/MznHfeeXzoQx/isssu401vehM/+7M/y7ve9S62bNlCX18ff/d3f3c2D+lFgzO12nmhnuNAeQHpWa3lzxbaE2xbK4xuxXy09MNJ1xvfeWo71+3cBcBstcB6by4J9wgdDAKJYaZaoK+vjlOPEWuakF7+PMmfdAyZwXpSYYkEu57dxOsufCyRVygbT8a4UvFYZQwA240Yy9UIWp10IgYTS0IJm90yfVaToXSdILZwLI0lDJ6MOVLrwRhYl19MPJNtQaQTN+Uj9R6ctOKi7AzvH9tNI3aYC1L0OgG/tPER/uf+i3moNtx5e5RatojTKqwbq5W4hSGjHIQlXhByDHCsuUC/myRSHZuvIKXEQhA4AdlW1ekLdz3aJcg/Qjhb4/DsVJnF+RprNw1ycO9U8t01hsBXoGF8Qz+5fJqJg3M0akEiqbUEjmPRrIcn7K93IM/gaA9KaQ7vSwiHEGA7koXpKpYtSWVcPvTxd60gsL/67k9y7NBcQsDbJ9+00y41jmvRrPtoKaguNjoOF+mM1wkpOR2eS1DG4WqZwXQW6YpOZVcbwz2Th7ls07oVoRcTkyUOzZcQecPi+ckYJKNESoFJyLEVJpVjuw6pWciVBNvPHeOlbz6Hvzz6CPuOzuBiIVxJoGPWHPY4NrNIGCriOG5pytOMjvTg+9Gq5H73ngk2bRxcIbOYnatxbLJMT3HJ3m25c8WpZA83v/2NZ/S+nilOJd0YOE2C3guJ06X1dfGjhbOuQX7DG97AG97whhW3ffjDH+78P5VK8X/+z/8524fxosKpBq7nS5I//NVvc+ujTxDGCltKiimPWhiRdhzW9RYJlUavVnEypjNhCS2wY4ESGkKNySQ6iv2zo3zpgcu56twn6c9VqQcelkyWSaPYYqGcSxwmKhmUtHH61KrHaBxgUCPmLEQoOBIM8Y9HzuPykQP0Og1KUYZnKkOU4hy2UKS9iKYGV0AEZLyInGdwpCY0kleOHmZ/lEcZh7BVmLKEpi9VxxIQaBtHxlhSI4XhSC1PJUyDgH8/+CwNY1PXiR1FQyc66P8wvpeHnxkEk8RSN+utAb9lw9RJLDEQG03QjJGO4Je/+hX+7Ko3ft/EdSzdRymskbVT+GGEY1soofGiZGLseib/aOJsjMPtimx1sYHj2tSrPkpptl+UEK9apcnhZ2ewLEmumCIKFVobegdyeOmA8ny9Q2ozuRSbt49RXWwweXhhxfPEUZKgqZQmZckTkvWMaZWfl401HQiJVjo5EXQkUxOlU1rAnQzLgzKAzuVttz+0gmg+8thhKkfrHI3nyTkeI8NFioUk5KfHS1GPwo5WuFxpMlWq4DQE/jaBbQwmNnhNCPItd55sSzKWSQJASq+Q7PB7+fPf/mkA7v6fz1ARdap2hF0yDB9zKTQt5qq1pBFPaSxL4boZfvZdr1i14v3IY4d58OFDaK1JpVxGR4oEeShtlZSjgJKe5hxRINuUK5wrlssegM7lajHQ3y9OpVf2Bjd2gkIyuTSNWnNFgt4LiTff+Pof2HN18a+PfzNNel2cOV7ogevDX/02f/Pg7nahlkhr5hqJrY7Smr2zc1gi8fxcgdaEJZRBhjD0QEA+6zFpN6m8JL1UYSbxRN4/NYYtYd3gMa67bBdBbBPEDp4T4dkxX39mB/64jXM6PWNIS+cMR44Mcigc6FRkCm6TDfkFRtwSG1PzHFaCl7gxrhFkWy4aBmgYybsGn+X/ltewNygSxDaxsVBGkHdDGpGL0hahSn5CltAU3ICpRvI867wqdWNRTCm0SbTMDWUz5tWxHU0cWtQWU0SBk5Di5Zxft95oBZ5j47oWZR28IPKHn1x3BX/69FcB8FwbX0cIaRhZTDSpXc/kLs4UxkB1scGhfdMtEpwmDGL8Rsir33wxt3z82wBIKyGpkJDqWsVn24VLRK1R85k8ssDcdJnpiRIqVp3HJP14GiGS2Oqxdf3YjrVCGuE3IhzXStL54pVjkI41xhjG1vczP1OhWQsw2jzn5rszCcpoV5nHe1NUR0MaccSBw7MMj/cgPcl7z9vJbfsT/2nVVDx9aJpIaHr3wuJFAicEJeDSnmP89PlPMpavcKSZ56/37+DRiXF67RRx0fB4fpHPf/d+9t41wf77JskpzYDn4LpJ6t9krYwxkM+nOtH19brPZz7/XW7+g5UEuX3MliURQhDFij1zs9RyFpYUFDwPY8Oj0QI7C4Pc+K6rO24du548BIEm7TmdE4GTOVZ8v2jrlVeVbmzgeTtSPFd8P+4XXfzbQ5cg/wjiuVjtnAluffSJxGDBksTHGfsbIIgVlhCkHJtG1PIh0ktSCic0aEuQfcMcV573FH3FKvPVPHfv25HYvrUhDRrBwclRvnT/Tq7a/hT9+WTbrz21nf2l0STauWFh5RTHF6yNTu6zbYVVA+1a4EtYUOi+hI9XmmkCBOetm0QZwaEwgzANLklFicGGTCb+nKWwTZM3Fw/ylwubKIkcjQhMS2CttOgElUArmtqOQUDBaVIxNjkZ0TQ2QhgydoRRgolmnji00EoS+UlVWcSJrMJurw9bCRmwQoGXtonQ5Egio//XHffQl3/+Fm6X9G/i/ee+gb8/fC/zxRrhtGZocZCCzlP3u57JXZw5Qj/i6IFZLEti2RIVa9IZl1TGZc9Dh+jpz1Gv+S3dsMOa8V4OPjNNHK1cAUpnPLyUw8zkIpDIMLQ2CCEwxmB0IjdKZ10KvVmMNiukESNre5mZLGF0ktbXqIfJyboB205szbL5FJYtqZablOZrjKzp5dqbrjzj5rvRkeKqTg/LgzLaVeZ+kSNVctiXr1BxQ2qzTT72ljdyxeg6dvQPc/N93+XJyVloGsaPuoj5mEpVE6cFl49O8ss776MR2czX0vSnmvzG9vu4ObyKJ0rrkLHBkZKb7/seL10cBFqyliBKdODtkwoSCZuwwLYkxlg8u/9EnWz7mMfHejh0eAFpCSoDoH2F4zisW9dHsZCmFoS42cwKKzunRxA7kjBWHDw8z4Z1/ViedVKbtu8XZ6RX/gFEKDwf94su/m2iS5B/BPFc/SVPh7BFgGGVFUyRNN8pY7BJqtXGGBq1INEPhobIk2zpmeDaKx8giCzKYYZs1ue6y3bxpQcuZ//0aEtHnJBOJxsyWcxx68LF6GMW4VQKVXM7zX/BsSyp9VWkaxAtCa/RED/lYioWG3ce5cq+/fSkG5SCDLumNvHk02tQTYkGtv3YDH5so0TSBv5sKLnAK2HLhNcbAbYx5ATYVsyO7CR76qNokydQNvXIRYol/35IPI79OPk5jWQqfKsyyjv6DmJ0jG8sUkLhOob/NfkSLFslns8CkCBjkIHAkgI8SSwNMjA4SCI0Smg2xf2Eccy+iXl2rB+mv5BhodJ4XlXlS/o3dWzdHtx7pOuZ3MXzg4B61SdXTCfVW6UZ3zDQ0fZuPHdkhacwgO1aCGDy8DxTRxeIY41lCdZsHCSVdqmU6qhYn9DQJ2Xie1xdbGDZcoU04trrr2T/U8dYmK0RhTGm5X2OAMe1kVJy7PA8Q6M9/OofvuM5OVLoYBc0Ps+vvutZntxn8d3dVzC5sJVGMzwhKGN5lXk4yDAcZDDGsFCqd/oHrhhdx1f3PET/4ghHJ0pEsSJ2LHLPaEoXGt6x/QnqkU1NuxgHaioZw9+6ZTe7712D0hpbCIKUJpvxkAKkSKzvmn7SiNhGs5nY7DmOhUB0+j+Wo33MQgjWr4Op6QqRq7Bi2LCuv6OhXl5gue32h2hkNcoyLIoQ2xJ4SI5Ml8j0Jp/1Wz91y/PqfWn3tTw9PUsziknZFttGhk65n+divfaD6M3p4kcHXYL8I4jn4i95JnBtiyhWWIgT7jMmSaETwHA+x0Auy1ytzkQlgEhjbInV1Lz8qqfwjU1gkgE/UAnhvWrbkwlBhqTi4wXkR+s0HYEJBcLRpNbX8Q+TkGRAVVz8/XncMR/pKda581yRP0Dfy2oEkUO+t0HDuFTxyGYCXj/8OM43YNfm9cR5yUCuSk25ZJwYYyA2NpJEw6hbx2FIrEgtDLGRbEjNsVDP8WxpgCiQnDs0ixAGZQSWMEhhmGokZvspO+ZJv4e/XdjIa/KTDDo+s1GKO2vDPFQdSpK0LIObiVBNB0/YNFMxkTRYQfJ++y74QpMyhpfEgwzoDI/NTpJynU4z3QvRVHc6z+QuujgZXM9GSEEYxKQzLuMbBij0ZmnU/KRCu4qnsOfZlGarVEqNzn5ibTi4dxrLlngpJ9E1lxsrbOO8lINlS44emmN4tGeFNOKCnZt43++8jb+++Zs88dAhpBCk8yn6BnJUK0mankE8Z7s2HeyC6u+DyJDJjnHu5gVGB7/J33wtxvMuOKHR7UyqzLBESkeGC+w/MEsYKjIB8CisfW2VmTCNaQUFGQl15TBWqDDr+khbgIIe5TLtNZi5ApqewapB7hkolJPqOyRVdz9I3ESiWLF1y/AJr3H5MRcLGYqFDHN6GpOhQ45hZYHlyflZDhUCbCEo4FAnpmEp4lAjI5t9s/NESnOsXOGpqRl+943XnBEJ/ey9D/Lxu+8jUDGx0jhSUrctDs4trOihOZ7kqjue7livAZ3LL3/i6ysI8tnozeniRxtdgvwjiFPqtZ4H3n7hefzNg7tRx8krkvqrQBtNTzrF7/zEazrP8X8/9y989k++wYFX54kcSV9PjUqjNeC2K8GxQ3++ChIsIUkHINf7EAmE3YqVVglZ9cbrGJUQYu1bhNMpms8U2DQ0yY9f8jixb1NtZFg7NovjKPyaDaEkEC6kQna+Zj8PP7aWuChZjDPkbJ9G6JKyY8bdBq40WCzJgdv9PhpDoGzSVkQ9chhO1cj1+MRaIoTmvMwir8rO0CtDDhfy/J/ZzTRiG0cqnmr28FSzFyEMltBE2upUtnQsSaVD6mWHpht3GvNUCppGsSaVp1wLiCzdiYz2w5hzxlcmm3Wb6rr414IQgu0XrcNvhKQyLumMtxTv3JIv3PDBn+D2W+5h6miJdCaxZ9MnWQZXsSaOFZYlMSaRWggEY+v7Kc1VadRDMIb0KkTvgp2b+MO//nk+8v5bVlStR0g0zj39uedEjgFofB5EBmQOgFx+gFw2xS//3Byy97oTNn/rtZfwiU/dCST65NWqzLBESouFTOLbrDRxbEjPwezBPLnxJrXIpccNGM9XyTgR1djl0uFj3F9KbDTLhNzfN4sTSuy6JnYN5Ysgs9ciNWMRRzFKG+LYUK8HeK7Nq152zhkd84hyqK63qQUhYaw4XCrTjCJeMjbMroNHWOjRiNjgWBYOkMLCVzENN6YahDi2xLUlyhhmaw3+7K57uOU0c8+ug0f4+HfuQxvTIfiR1kgjWGg22dDX24mrPp7kPtUfc0mQIVtf2t9q1ms/yKbCLn400CXIP6I4U3/JM8GH3vBjQKJFDuJEP+hIgWc7OJakkPI6VYLOEll1loW3D+EHIdgtr+NUkyBekn14TsR8NQ9CoDD4ypB3FNIXSeU2I1r+qBqZ1ai6wAStqvK6Ov5Bw1Xn7CEK7MQSzhJYjkYZQU+mSVN4YKDfirns4gV+7Nw7OOwXuKfZQ1+xRqht+kTEzlQD3Qq+cwQ4JDKL2IBvwJGKhnJwpMFK+bhWTNoxbHIqXJufpBx7TEcZeu2A94/v5pNT26gKgS0MkRbYMrGIO9IoYIyAWKI02FKjHDo2dR2LKgPTcR0nYxHHisfUDD9eOIfzNgyfsPTcbarr4l8LWhveddM1AB0SfLy2d7mn8EfefwvGGI4dWjjpPl3PwXasxAqtlcqXzaeYmVxEiGS16uC+aW7+0G38xH+4YkUy3rXXX/l9J+GtgDoCcuUJKSKT3L4K2il6p/NK3vqqcW6+73ucN36EX3zdY6x3q0wey7F77zA9eZ+tQ3PESmJJhTISjaAUuvzG9vv4vSdfygNzY0S2QemYXNMCY5Jqs4DFDZorM0PUGwHPlEvUhyQ6LbADwye+9C8AvP26nac85p+99hWEecHNd93D0zNzpB2brUP9CBJyqrISsQAxutMEKBRENqQsiZ1kgWMLgbFg7/Tcad/qzz/wKJFSpB2HZhwhRSIJiWKNL+KOxGM1kpu2HZ7ujxiuLw2Oq1mvvdC9OV386KNLkLs4I3zoDT/G63dsPaU+rL2EFWvNbK2OsAUpy8GPYr7z9A6uu/S+xJM4WnKm+MbeHYmGThscJXBKFrGnsIJE8qDSAum12GvcSpdrNbW5wz4D2QrVehrtJoNypC1sqXClAgljts+r8osEymK+7NGfb/JTxTL/d36Mgfw8l+UqhEZQ1pJeuVQhF4AtICtg1ArYFwxyQ99ehl2fudjjrtogr8rNYoQhNEl+dkM7YOAneo/w0YmLGM1WcC1FoGymGnkqfgbqNtoC4WhiJZMy/HLS29IlR1pTcD18oEHMEwvTjMo8c5WkTJJJOTT8qNtU18W/GgZHilywc9NSGl3re/zsk8cSwryMuF6wcxNTR0r0DuROuU+tNOdcvJ7qYoMDT0/iphyOHpwj9COEELgph0bNp1pq8P/9wVdZf84wA8PFTjLeDR/8iRVV6+fajLcC1lrQ8yCWHbNpJLefBBedv+6kMcwA904e5u/m9/Cq80r8p3X3UA0tJuMUY2OLXHHBEY5OF3l6ro8dA3PY0lCObA7UiixGKXJ2yLvW7eHB6TGQSaBIYuMmcR1JEMWoPBSaafYuLlBdb6E8Q9yraHqGWiT4n3fezZbNwyuO8WTH3PfAo5w/NryilwVgTsLwaJHKQgM/iEl5NoWhPNXF0olNKm1v6hZOpgE+WiqTdV0ipbGE7PjpK2OS5u+WxGM1kju+doB9+6eoV+qntF57oXtzuvjRR5cgd3FGWK7fWt/X09E1L29yaJ/dH1xYxJYWtiWwtMCP4sTr+MGXctW5e5acKfbuoFzfhG0CjAE7LUntdqm+rJGECgRgaYUqGlTDWjoYkzg/iJRmvpYnn/IJQgmuYTHOMORVUCYxFL4kW0EBM34WZ1FT9xwcN+Z1g1P8xfQ23pCvMK9tCjJAWsEKlbUiIcnnWJqtPTOERlLRNnkZ8baeo3giZlalSNsR1ciiaAWMunV2WCU+yCP8/exm7l9YiyU00jXgS5Sjk8ZGqTFA70AVFUuadZcoaiX+JcUzYq3xwxgBLBQD8jEsDE1Rymos32FztJYPXNFNveviXwfprMfu+/fzqY9+hVTGxXYkD333Gb77jcdJZV3WbRpaQVxH1vayOF8jnXFpNk4MCgGwHQujDZYt6R8u0DuQZ89DhxCWwHUdwiDuOMdobTi0b4bJw/Oksx7Fviy333IPv/6n1z8/Qnw8Mu9MNMiapHJsGslf5n3PaTfLE/ie3lYnN5jm32/djbCzSONA5NPTGxApSW+hydSxAYI+m4YSRNoCBC8pzpG2InRecvHQMe5fSKQWUrY1xwYrZWHVYKFUp9SrMSlDOKQ6iXzGMsxuVfzFP/4Lnzz/p0973CeruKZsC8u1GF/Xt6LHZctAH4dKiwiVNBwrbYi04iWjiSTmVBrgNb1FYq2ZKFdwLIkftZotpaAvne700Hz+gUdPJLk2OMM5vneljVPyucAv8vP/6fUn6I8X6g0eOzZN2rFZ21vEs+3vqzenix99dAlyF2eEM9FvtQfUZhThWgmhtYTAlpJYa/bPjq6wdRNA2olRWpPBJi8dtmbW8uTdh2hcEGB6DbIkiRfBOO1ZsVWisMH4ku/u2c6bL78ficBPC7QR1OIU1dgjbwfkpGYuTmG7BmtLiLVgMBgGrIBYW8xGHkN2kwE7OqEFMTISbQRrvCaTUYpBO8QVSYhIVdm4wuAJDULQZzdZ59WSeGwtuSQ3y8uLUzxR6+N/HTyfB+eGcfIxjm0wik5jo1YgLc340AJDbo2UE9EIPQ7ODLBQTZZNs9qhma4xnV9AKkEKj/WbemiqEqI/OkufeBddnBrNesDHfuUL1Ks+jmMRBBFK6aQpzI85sn+WdZuHSGVc/vrmbyKEYO/jE9i2XJF4B4kdW89AjpE1fR0btnfddA0X7NzE2y//bYQQBH4SWb0yAjuJuI4jxcyxRcIgfsFen/QuR/PfEy2yOpJUjjPvQ3qXr7p92/Gis61zKYtz36EY7+XNr+hj156XsUs71CcCei+ep6YLgEYYQcqJiZoSz42xQvBVEkKUcyLOsUsoIxK5hRH8xo77+N0nX8r9s2OEJPIGpMIA58zm+NTN7+GK3/kElXwjIce6lRSqQFuGR+TsGb3+k1Vc26uGx/e4APzmV75FuRkQxDGuZTOcz3HTqxJ5y6nmkHdediG/+ZVvgQE/itHGIIWgP5Nmw0Af77ws2f/xJHeh3uBYpYZjSfKDWXo3DDIpJcH6QueYl69supZF2Q9YPDadEPVspqNt7uqQuzgeXYLcxRmhTX5LjSYT5UpLYmEzU611tmkPqGnHSRL3LEEQK0zLPXi11bdGFGFLQaw0m8iR70mznfU89Q+HE21bpFEXCsy/i9BWKzXLAmFBtD/FvnqBL993Oa94yVP09y6yGKX5p+ltHPIHKHhNXrb5LtY6PpZoEjqSeddDK8FcKUV6WvGdzCA3DO/rJHq1VwQTPbIhaIV09Tsh2iS+rFkZk5cxsYGCEVSMy4DnA2CLpJoTaAs/lmz0Knxg/aN8Ql7IfdUhVGCw0xohDUInvsoFp8Gm4jyxkgSRjWdHnLdmgicOj1NZKBIaRbqvidQuFpIgjMnaSRPS3x++t2PZ1kUXP0jMTpWpp33clE2j6qO1QRvTabKzLMnRAwkZq1d9cj0Zhsd7WFyo49gWtmsl29mSTeeO8s5ffM2qld+N546wb88x4lgjZfIbbEPKxOfYsiVKaQL/hT1hlN7lcBJCvBzLHS/AgeC7EPwjnnBQepBitsEbX/ZVHt9zJffMDXN4MctwvkmlKUAK/Mgm48T4YTIll5seG4pNLAmxFvitKPv9tSKxkbxn/R4ePjCGFUBQBOxkVe3IQIN7Jw+zdWyQmeAgctn5ggZ29h7jPVuepHbkG2RymyHzzpMS/lO5IZ2sx+V3fuI1J7VRO1oqY0nBwYUSzSgm7diMFvIdDXAjDAnipHJsS0lvJsUfveUNK+R7Gdfh3KEBDpfKPDk9i9IGz7JIuw6RSirQ48XCisLN5x94tFOdtoQk57pUg4BmFFNMp7puFl2cFF2C3MUZYU1vkYNzC0xUqlhS4LaWwUKl2HXwCJdvWNsZUPsyaY4ullEteYUjJQEtL2UBaIMWSXVZaUO6R2F6a+z1FpkK0mSfSCEwuNMVrEDhHoqoHQL/ugKmR6J9iX7SwT1mQ9FwYHKUksqx3poglQrxUopC3OTV/QfptSNcqYhN0iy31qujjaHoevyXwhN8pz5ITTukZIzHEjlOUp8NtmgVrQ0IDClp2n10iTUcgthARmqqsY0nFQiBtAxKCxyhCbTF24ae4b7F4cQmQyQTvLQ1OrIYL5SJtUiiuo0gji2kFGwZmOfh+SLGAoHGMhKlDSk3kWJkLI9jzZM3PHXRxdmElIJ0ziMOY7QBIQVCJWEdliVRStFsJNphaSUm4zPHFnFdG8u2yORSK3yJd9+/n4+8/5YTtMvvuuka/uw3v8jMscUTk/KURlqy5Z1sSKXd1Q717KPteEEMah/a+GgDlh0zODjLYi1PMdXko1d+he/NjbNreog3Z/eRcWzq2qVRz1LoWaQ0l6aQ9xnP1YiNhTAKIQxpS7O/lmMxTCGNYZ1XQxrI7YPoQrCC5KlDR/O799/J2y45jwfumyCwoqRnQxh2Dk7y/1xwH0Hssu9ghk3rJ8ip30fz31clyc/HDelkxHnXwSNMlCuUm36nCNEIQuZrDVKOwy/eegfNMCLjOcm8YAy1IOo4YKyoPnvQl03zyNEpKr5P2rUTz31LgJLM1xudRkFIiPl8vYElJLYlqPrtJkCYrFQ7EpCum0UXx6NLkLs4I7zzsgu58e9vbwW9JUQNAaOFfGdgWT6gBnFMM4qTpTbbImzFUNu5EDnYQHoKEdjoRYnsC8BIwsiiIQIWt1dZOwFDvsPTDxxOIqK/JRi6r8rarWMcmK4zd34fMgsZx4KtFc45/wixEkTKwhaac3pn+PGeCSraoWFs+qyAlFBYrU54Tyq2pytsTVVYUC6VOM08mjGnmfgakxDjsnJxhE4ItFiy2k+SoAWl2KWmXXY3PIadBj0yTGzcjCAW0Aw9GtpiLFXHUoLINomThTCIFlFO2RGRkhgjEK2Y6VhJ0pkAEUHqqMR4gkq6iYPNhuEkJKGhAsbSfT/w70IXXUBSvR0Z7+XQvukkgbI1JhgDtm0R+DFSJlrhdMZFG0MUxMSxIptLUa/6HX0y0NEy9w7kVmiX2z7H/+/v3c7Bp6cQUiJI7MC0NliOwHZtBkaKjG8YOM1RnyW0HS/iPURaIIzGIJAiMYsc6VnADz0sDGsIWLfxWW47uIUrBmfZmKpRLY+we+9LGcg9zfnnHEbHLpPVXnoLJSwrsYHstUOmIkHWiThazWHXobalFTSkE+mJUIKM7XD/4lF+5aVX8UcP/wvNZoiI4T2b9+BrFzvIIaVgYipmfMTm0DN/wM1/+6ZVXTdeCDekdvU3iOLO+NkuMrSvVfwAMHgm8dY+3gFjNT10qJLyuDIGuyW7saSgHoYrGu/W9BbZP79A2nFa2+ukF1okBRzoull0sTq6BLmLM0oXunzDWvqySeSoH8ekHYfxYi/FtLdiYDl+QL3p1jvYdfAIKcci8ho4a+poBSaSGFvhrA2gKRFN0FKxeXiA/c8epXSh4NyFvfw/f3GYkXUBs8cyfPEf17N7pI85NMq28GYi+rwZNl1yFDulUbqV7GcZLGnot0LK2iYwgrq2WevUcUyMJ5NwgtAIbAyDdkBZaSra5dgyrfHhKMvXqqO8IjPLkB0w4jRbFWZBqAWBsfCNxaDt8y+1AS7ILCTVEQNSGDwB80KQFjHH/CzCFVgIVCyw3SRCF5IEPk8qotBCIhKCITWpWpG+Qx5RrDAH0nBBA22SCl099mmqkJ9fd8UP5DvSRRfHQ2tNvifD+i3DHD0wS7XSxLYlo2v7WSzV8Zsh2XwKIQRxrGg2gpa5uKFWaZLNpUhlXG6/5R4AUhm341/cvrz9lns6VnHj6wcw2lBeqFOv+ahIY7uSdDbF+Pp+/EbIjkvWr1qFft6vsa0rjp4EfBBpsLedKE1oO16YJk1lSEmBRWKJmXUSEiakIgw9okaKTYO9vKmnxif//p0rgkWmZ87ng//x0wRRD5ZlUY0FQwMzxEKQtmMydkDaibnl8UvIPwulC0EaCDOJvthBECnF0zOzDNwD51YzPJ2LiLOwNl2lUsriyUTW0miE7D8U0pNv0NebpbTY4BOfupMbb7j6lE4cbZxu3vjsvQ/y6XsfYrZWxxIi8TVmJTmWQhAqncSKa4MfRThW6/1Y5oCxmh7ateykUKGTkxBLCMJY41hWR7MMSXHnngOHCWOFa0sEAo0hZUtSTkKBum4WXayGLkF+keO5pAudOzx4wiBVC0IyrsNNt96x6kD5zssu5K5nDuBaFtZIgFYk/kTGQJwscxnPoGOBMHDoyAwiEmxcP8VP//Ih/IbFQjlL/hzFja/Yy63TIU+WetGWwMvEuG6EkBoTgdOeZ1qj77xyycoYC02fHZJvCfISz+NEcBwLgYuirNxWpVnzbJhnX5Bli1fnusIEDSVxhCbUEldqZEtqYWnNJrfGfr/AtlSVqShFnx2SkQptBKERFO2IqtF8ZmorWipsIzHGQkUGYgvpGI6We9lcmMMSJjkuGWM5hmMPDmFJie1KNhVGoBYykZ3k4OI8l6/byM+vu6KrP+7iXw1aGxo1n1whzfpzhinNVekdyOM3Iy48Z5jyQh1jDJVSnSP7Z1c0IWhlqJabHHxmmsJslUw2dYIFXDuyuo2pIyUGR3oYGktWUCqlOtMTJeq1oEPCP/sn38BNO4yv6z+hCv2cX19bV2xiMPOt46+BSEH1OGlC2/ECB0wDbQSOBGWWXM7SdsRcrY8N6wbQxpDPHObpvVOkUg7FQopyxcf3I5ROsX70KJbUKO0irVEyVolQhTSsIn/9+AWUH15DbzWgoiL8Yuuk3AictM2T87OkKoLSYpahOM3CY+Uk8nprkWK+SbMJnuegtCaX1ZTr/QghOkT9ttsfOi1BPt288dl7H+SP7/xeInVoJ5TS6u2wJFErdEq2Kr8516HqB8Q6WeFb7oCx6+AR5usNHj82TdpxWNdbxLUtimkPY1xSts18o0kjjLCl5L9e9dIVc9flG9byX696KR+/+z78SJF1HZpRBEYwWshTC8Kum0UXq6JLkF/keC7pQqs1bczW6p2mmeMHyvZ+pBA0o4iUE2NrG8taWtoyBrBamlwDJdfgKPj34/tpLlo0fRerqNADEGrJ1X2THGk1qGmTaJqlxYpvsmjJz+6uD/KO4iGKVkxsBJrEdlgIsIwBYfBkUs8Yd5p8urSJ/WGBTW6F6woTBEZSUTae0NiojvQieW6wBDgoeqwAz1JMxykWVIqcjBmwfTyhkMLw+w9fybP7xhDn1zGugsgimisQ1S2CjKZMnng2w9rRGXLZCBVnmJoYpXI4SzZlMz5QpJhLgZ+i0MwzX2nw0Tef3qZpOR7ce4Qv3PUoE3NlxgeKvONVF3bt4br4viClQAhxgutEG20LuNJC/QTXijYadR9jDH2DeZqNgDhSTByco1ELkobYQord9+/ngp2bOjZx7epyoTeL7SRuOc16wMJsFce1QRsOPzvD+i3DnQr186oit3XF+iBggbQTsqznwdqQ3N8iyB3Hi9rNeOphBJpAWVgyqW5qI4iMy/CAptKEqalj1INeNm8a5MCheY5MlEinXV73yjq9hTqOFWPZKTKWAGbAGsbp+12uXnM5V18AvCOxj7vhu18ilD4WkpRrIy2JH0VI6ZDNeDz9zBSeZxMEMbd9ezu/8FP3JRHUPuRzMWnPcOdDSyEqmbTL5NTppQanmzc+fe9D2FLiORahshJf49Y8sbzJUhtD3vPY2N/TabprO2AUPI+qH3DD336JtGMzVshRavo8PTPHhr4eBnJZZqs1Fv2AnOdy6brxVVc/Ad5zxaVsHxnqVLwzroMBmmFEXzbzfSXNdvGjiy5BfpHjuaQLrda00V4FO36g/LO77kmaLlyHcwb72Ts7hw4tpKMJ2o3mAnRkIV2FwEBsEA7Eecka2aDUdMEYnMEYg6CpbIYyPqaUVB1sVxPFiU+o3dIWL/dq2x8WiIzEQuPIdnR0Ak+ozqahlmjgusIEX1yEq7KzBEYSGIusFdNnBeRk0ojka4kjTKeZL9A2obEYtZr02gGBsZmLXQ6HWVJCMVEpcH95lIudYY7tqbIwHJC2bVxtsDIGy9IYDEE8wLMH+xG2YGSwwG+98mpuq+xmodIgu6zx6Pkk5z249wh//MW7SbsO/YUMC5UGf/zFu/nA27oeyl18f2jWg5NWaNtR0x++8a8BgZQG3f4BtgmzhqHRHoQQzM+UmZuqEEUx7R9y0Iz4s9/8Im/86SspL9TZ+/gEqbTD6No+HNfGb4Sksx6pjEscKRzX6tjATU2U2LJ9bEUV+jmhrSs2TaD9G7SS66sk6iWOF59n/+TtrFH/DxKDrzwqkUe/W8W1XNANFhamSaVi/vmRl1MsZEinKggBKc/hNS99mHqzQL2ZYaCnjJMzyXOqxaRq3VjbkXdcdP46hg8UGRc9HKtXacaJvWa6ITEtS/Utaw7ypqufYKhvkWPTeb51z1Yu2DrNyGCVZjjEbXdeylRp6bNrNENGR04/vpxu3ig3fdIt+ULKsamHYecjj1tWnYKkmryhrwfHshjJ5xjK52iEEWnXYa5WZ7pax7UsNIbJag3XkhgM++YW2Nzfy4b+3hM8+U8m/Xgh02W7eHGgS5Bf5Hiu6ULHDzJv/dQt2FLy+MIMzSgi7TiMFfIcmC9x7vBAp+t4LChweLqBWFtPnBy0SCrHGsLDaayiQqRipG9zfjRPb8Zn/KIqftOh5NhUYpeUrViI3aTJrVWFkNJAK2a1DWOSgfiV2SlGncR+rc2d239tSzelkuvTcQqD4Or8bEe7nJUxI3YTZRIpiBTgCY2vLUIjkcKQthRrrSpg0EgcFGNOg3mRohk5fOaJi3Aji+HeHHZF8odvuorPPfUQdz1zgILwOC/qAWC/tUjDirAD+I2dV3PF6DqcVwn++Iutiv33kZz3hbseJe06HaLdvvzCXY92CXIXzxvSkqet0F6wcxMXv2wLu3ftx2hDvWUHR8s+MZtPMTBcpDRfo3+oyMxEGYFI0uHSDlII5qbLfPoPv4bj2LiuRRwpDu6d5pyXjHPDB3+CT330q/QO5PBSDnGksOzENSNoRjQbASNrep/fC+wk6aXBhCTTpUquqynQM+jpy1rbngv5xCP5vNFrmZv8MvONI5RCi7Tt0GsN4sgpQBEECtv2eMPLvkqp0svnm5t5+tA6giCit1CiWs8BKQ4eS3HBjjxEe4EwIet6foW8Y12+yFyzwfkDI53DfmzxGFYAG0b38+pL7qHp25QqWQZ6Q65df4Av3vlaFh7awVuvvYQnnr2TVCogk3ZpNEN8P+Kt115y2rfmdPNGMZ2iHoR40sKxJJ5l09BJZcS1ZOJsgmB9Xw+x1oxk89z0qis7c8tNt95B1Q+oBhVU66zKAH60dG41UakSa00lCKgHEb/8pa/zs1dcwpcfe4pYKeYbTQ7Ml/jOvoOs6SngWNaqWukz6cHp4sUJefpNuvhRxjsvu5BGGFELQrQxHT3W8iaHUyHjOuydnUsaICyLMFbsnZ0j1ppMy44MoBL4pKM8TBURcVJJNpHEP5wlnkgTPJUn3NPPzmaTX77sIaplB6MFrhszmmrQ7zRJWYp/Lo8grERGEUcycYIQBhULNAKtoRa6rJENfjw/2SHLFpzQICIAKUEIjRSGAEmfFTKvXDyh6bMClEn2a0wiqzCAKzSWMFgYBIkrRdPYTEUpAmOBEWSE4u9KG3i4McgW3dup/F4xuo6fHb+UtQdyyMcVswdqOBXJ5dEYO8uj/JiziStGE/3fpVvX8oG3vZK+Qob5SoO+QuZ5VX0n5spkUs6K2zIph4m5btd2F98fjtcJr4Zrr7+SXCFFFCmEFEhLIKXASzms2TjYIbHNeojj2eR7MmQLKRzHQilF6Cu00nhpB6UMgR+hjWHySGJxOLK2l2YjYHi8F6U0KtaoWGE5Er8Rcu31V57y+Npo28z94ts+zkfefwv7j7w6Sc4TfYAC3QBTA10CtRdMdWlAiZ+A8q8numWgr++9nFPMcPlQL+f3D5F3c2CNQfYXyecU2hiq9RzZVI0b3n4PW9cf5uLt0+TSVTaNHWB04Cg9hRDUUZK1qhiiB0EdTGQejc8D8O5tl9CII6phkOwzDMj3phib8ti5/bsYk6LhO8n4RYZG4PDyC+7tuFXceMPV9PZkWCjV6e3JnHGD3unmjZ+94hJirQkihdaGME4SQTf39/LyTevZPjxEynE4Vq6uSkqfnp7l6GIZo/WKMbu9CigFaKPZP1+i1PBpRhHT1Rq/9827eXJqhqdn51lsJvKdZhTx7NwClhQdCeCug0n1v62lXqg3VkgE2/d38eJGt4LcBRnX4amZOYSBrcMDz8kwPanMio63L8IgEHi2RSOMyHkupYbPQsNHa40TOZhSDypQGAnGEthOsiORsvjJS/egGw7lSgrftxkaaZBxDDlX8emnN/O06SXT5yMsWFOqcfWGKYa8gNnI4xvlMfaGRWxpuDo/gwUERpBeZs8mlx03JKTXljBo+0zFGRaUy931Qa4rTOAJlVSKSZpM7FaV2hIGt9WhLoUhJZaWjo9GWWIt6LEiDsU5BnKGgXK2U/n9m28/yP/+yn2EcUysNLrp8+yxkLGBIkEYIQRc//u3rNAJf79V3vGB4gsi1eiii+NxJhXatk3bX9/8TZ55YoIwiElnXNZtHsKyWyT2piu5/ZZ7mJ4soZXGspNfaugnvQqWLVGx6gSBGKU7NnGvfvPF/NOXHyaVcVm7aZDJIwsEzZhzzz8xfGT3/fu5/ZZ7TnC5WB6Z3baZ+7PfXuB9/+M9bFr7TxCUgAWSKbNN03RrScpJlrBMpaNLPlkKH43PUyj0MVdqIqUhIIUg5vo33EcxDw0/jef4uFbI2OAU6Ii26zrGBUIwExAlK2NXjK7jN3ZezeeeeojD1TLr8kU+cPErSO2AAfG3zJVSZLMWmMR1RJs027cocmsTEnzR+evOiBAfj9N5JL/niksB+PS9D1Fu+iAEG3uLrOvrodTw2Tc3j0SgjVm1MbwZxQgEIvEJPOH5lYFG2LJ5a5HoNjoNgVrj65ZDBmZVz+Pn0oPTxYsPXYL8IsbyTuRL1ox2tFzPBc0wYstgH5OVKn4Uk3Js1vf2UG76NMKIih9wdLEMxiQWbICvNdIWCAUiTiqw0pMUM2kuWaNRwRqeKU9SDrIsHs6CMQwN1dh3qJ/CWsGm4rmMWlNcMfQ01abgWD2HqzXXZSf4s0o/5+dnuTgzjzw+O7qFpaCPhBwDZKWmICO+Vh1lf1jgSxV4V89B0lIRt0bfQAsckbSl27QbThISDoJRx2cyEmjpUorTmDgNjqKvkOnIIv73V+5DG0PGc9g2cog3X/woa3orTFd6+OaTO5mp73jBdcLveNWFL4hUo4sulkMr3SG3Z4Ke/hxrNw6RyjgIIWg2QgZGilx705IV24GnJ5mfrmBM0lvQjq52XJvAj1pR06CUIZ9xSWVcvvfNJ0hnPfY/NQkCNm4dOaFhEFiVBLddLm6/5Z5Vbea+8Jk6v/6nN6NLNyXyBpmD8P6kiosE4ycEGSuRYSzTJa+Wwqerv08uN8CGdQGT02WCIMLzcmzfdJhjc8PMlSxi5TE2sIAt6yyJwyTQaGnJDJgIHexCepdzxei6zqpTB6OgS+czNto65s4B1ED2n9HndTqcTtP7nisu7RDlm269g4V6A6CTaIcwpC2bnOdS8QNuuvUOYmMQBvw4xpKiFTndCms6CZavCJrjbjcAxmDL1T2Pn0sPThcvPnQJ8osYJzt7/rO77qH/FJqs5ZqtuXqDnpTXOTOHxPpt28gQ77zsQn75S19PgjlsCz9W+CqpvGoBli0Y66szsmEKL+uzY3ATxhlExlUiCUYn1elMJuLoZC+Tn1nDSE+Om772S+jSTRw82E9QkUgZ4WdjkIIPjD7K+kK1U+OxWse0/PpxvXzQup4WEW8pHKbPStK/FmKH0AgyUhGZZE8xhqkozYDlA4LZOMWInQz8yggG7YBFJflucz2eu4GXb1rLR9/2RgB+9VN3tMzyDdtGjvILr/4efuSw2MxRTDf4uav+mdsfz7N/fvMLqhNuSzW6LhZdvJCwbOuMLNSOJ6bNRrBqc98FOzdx04ffyuf//Fvsf3oSMOR7MmSyHpXFRidqWmuDFILh8V6iMO7okXdcvJ5mI8BvhKsex8lIcLuifEqbuXbDHrQ0yX5rK7XsUkG8Dz11AcgiZH4OmXvPcW9aomsuFHIUCunkNl2D2GbD+nVLFjxRM7HDpEqraYOl2mhrm+Ot5o5H23pOkzQVmkbyl3nf6tufRSx3QGqEIVYrIny8r0Cp4XNgboFQJwEeVusz1kYgW2mrJzFBWYHj72+P8wZwbXtVz+Pn2oPTxYsLXQ3yixhtu5vlCOKYx49Nn1STtevgEX7zK99i18GjHFwoUQ9C9i+UOLZYOUGLdvmGtQxkM5wz2I+UkpRtIzRcNjLBn/zYP3Lb2/+O33vtV3lJ7wx2zcNxI+5YtCnXp8l4SddzJh2SSsX8wze2E6Qd/GZr8lNHqNQkrueQdzL0hgWchSIbcxWUhtiIDhmGZKC0WPIlPZ4gGyBnacacANNa+huwAxxhcITGEhqFYDJKU4lcbMAVMf12kEguhMZpaZm/VFnHviCLEEd45eBQ5zn2Hp1NZBXa8JM7H6cZONR8B+wQciFRusFVF/4D5GeAF1YnfOnWtXzshjdyy3+/no/d8MYuOe7i+8bo2r4zsk9bTkyFFGSOCwhZjgt2buJjn3svt973P/jQx9/F+i3DLMzVgJbuVCe0Z2x9P4XeLJNHFnDTzhnte+pIifSyUA5YIsFtHfNyrJCPWGsTcgkginR0wSjQfuu+JsmUmgZdh9ofoWufXXkQmXcm2+paUgHQteS6vW1p/9ByzoClU/xlMgORBllISG9Li7wapHc55P97UjHWc8ll/hSE+iyiLcnoy2awpMQSgi0DffRm0hyYLy0lrUoJJJac2oAtEivBkywGnhZCtEiyYYXncVsr/f324HTxo41uBflFjNXOno+UyqSdk2uybr7rHqarNVzLxrNtlDZILZip1bFbXcLLtWhreot899lD+HFSOb1sbIJfe+k9NGKbmhTkZMRPjjzLl7VHytrAnFrL/5kIuLQ5zXB/lanZPP/wxW089tQI2gKvTeittWS8PZT9JnZPQD4TssbzsS2DaC3LwUoi3NQSW2i0Aad1x/Lmj/ZCZkaqzm0poWhqm6kgja9thIAgzGClm1hCYWMIjZ3ok43mSJjlkWoPQjuMZVI8UfsntlbH2JLfgR/GiS9orBjrqVCOHfJDVWwvseEItEW/V4cNj8DBi2jM9HR1wl380KJZD06/EZy+OrsKlledN507wsTheWQosS3J6No+Bkd6aNR8/GbEpnNHVjz2ZPs+3kcZlkjwtddfyac++pXO49uV6I58pF2NVVVQkyS2b+1KdYOlkUSRaMe8hNM2/j9YVkVe7pdM9HByo3UueNeA/6Wlai9Oa78WcJzszSiw16xqNXc8VpN5nC0sX1lUWnNksdyR7LmW5PzxUd73qis71WTHSvyRq0HyPbIgcRdqkWVtDIFSWFIgpUWklsbl01WUBZB1HWJt2NDXQy7lrep5fDotdRcvbnQJ8osYqwV/NKOYc4cGVmy3XJP11MwcjrSwrZYXsSXwjI0yhttuuP6E57h0zSjfePIZZKsKcP15j1OPbeqRS68V0IxtjIy5cuAAX5tbx1h/kQN2nrv+6lJiPyKOFEoZhCXYed40737TAfTcd6mWDdnUHLrHwsIw6jUSbaIGq7Uusrx6rAzsj3KM2HUe8tP8WLa2omFvtaqyBFJS4yuLPjckIxvYaCKvgTERlvTIyhSRgWbs4wjV0nI4aAwz8z7pQZe7577BlvwOUo5FpbXv6WaaTaNzFN0IVygiLCrKZtbPY5SFGnya5tFLujrhLn5osbhQP6PtTkVMT4bbb7kHpTQTh+YJmhFeOnG8yBXSFPuySdV3TS/nnr9mRfDEqfZ9KhLc9my+/ZZ7Ovtero3uENvyLwEGZB7keLLjeC8JmYWkiS4EkwE80CdZATJ1sM9dkj74X4LUdS2niiNgbUmcMZAkZLzaeqBMkvxkT1J9tl4YIvfIY4e57faHmJwqMzpS5K3XXkKYF2dsf7a8n8WPIp6dX3mCEijNo0cn+Y07vsnvvvGaFaRUisS200AnYa8NKQSebXPO4AAT5QqNMKQeRmckt3j55g1nZNnW9Ufu4mToEuQXMVY7ez5/bHjFNqWGz6GFRZTW3HTrHSilsC1r5Y5OcTr/4NFJsq5LEMdoDGsLVWbrifZOKYklDIGxGXSbHJwr4bmGPnuAJ7XBNwbLkTie5CXnHOPn37GLgeERkH3MH3uazKhGGJuRdIMYwUzoMer6pFvLkcsJb4TEE5qpKI0lVCK1ECtJ9PFo3+cInWiQjcBIgZAqsZeTa4AKsaoQasmMTuNJTYzBAmIvYnYhwPMSycRgT46phSoCwWE8rvR8FBAhcIRmyPa5LxoiCgRezu8GeXTxQ42o5SJwOpyMmO64Zj0fef8tJzhKABx4eoqF2Qq2beG4FnEYM3V0gb7BAh/73Hs7+25Xmo/f92qNg6cjwRfs3HRKyYj0LkfLgUQO0dEKP87K0aM9GLaqynKVFaB2Qp+JQO1pySkc8L+BHLils5meuSYh0gRAT0v37LbS/GovmJ74kccO84lP3Ukq5dDXm6W02OAjf/WPVNfbDBSzq0ZJH4/l/SyPTkytuK/9jkRaU/EDPv/Ao9z89jd2Hrd/foHmKk4VkISMCAQH5hNLv2Z08ibyRL+cRFtnPLfzHF108XzRJcgvchx/9tyuBFT8gMlylWoQIIVgfW8PC/UGBgiiGCFsLCFQxhArzY7RoVX3f7RUZvNAH8/OL4A2TJQLFNMN6pFLWPfIFpp4QjEXphFuzNTCAsG9Wxkb62HyWImGHxGrmDe/8nFy+V5yxaS6XS1BPOpQExbzsUc5sluCMx9fCzy51NgRGoHA4AnN1xtreHX2GFUtKUh9UqcLWBrYbTQV5TCjC617NBvcMlLPMKn6WAwVkYKMjChpFzAowFiaMPDp85L315A0NqUsyXn9Jab8NEUvxJUJAV/UHudka0xu7CfnFLl0Y5ccd/HDC8c9s+mjTUw//+ffYs8jh8DA4GiRr/ztvfQO5E9wlLhg5yb8ZogQomP3ZtkSpfRSD8Jx+z4Z6V3tWJ5L7LQOdq20aqNV8RUtyYhpkhDY9mixvFLQBFNIHDBa6XdAsi9jgd5PIixokd74sUSz3K4i4ye6YXusdTCLEB9M/i/7IfO+M9YTn/A6lh3Pbbc/RCqVRFMDZDMeU3oRU44o5lLsmSrRjGIcKfmzu+7hllUI8nI3iHgVstshyUpztFReUXHeOjjA7mNTJ9RYpBArXJXyKY+UbdNouVGs5lqhjcGx5AmroF108XzQJchdrMDlG9by5vO38fG776Pe6ja2hODwYpnJahVLSBQxEkEYaxxLMpjL8L5XrW711NY5j6WyHJwtccvj5/HLV94DBvymDbaNm4v55/IIJvCY+KdBxuwRbEdhRZoe10ELGBmtMTWRxU030K6g3udhRJMxp4GLZijTJNA2umW5Vm9aCFehkHhS0dQWXy6vQUrJBqfR8TRux1PHJmkCatu/QSLLCI1MTgqMRKNoOyrPK48x2UDoNNok5DhtKe6oDCUNfUYgjMZNGV458FoAmkHE5tE+phaq9NkBi7FHRacRdpxUPjD0WwGBDnhD6zFddPHDip6+7HPavlkP2Lh1hHTG4+nHjhD6EYWebKe5Duik8nkph2Y9QMUaaQm0Sn533nGBN/DcSe+ZQge7Et2xyCyl2JnZJc+xjla4DuRImvZ8lmibDc62pfS76Lol8tuOrxYmCR8hBjTUfg9ED1jrQfYuEWJrJNmfNfacG+1WfR3LHDAmp8r09a78LANbo0LFvlbAhmtJYm14/Ng0uw4eOaGKvLyfxZaSUKnOfSuIr0i2XeGg5CWV4ihWKJM4lBjoXBqT+NBHSpHzvNYYLYi0xhhNtMwDLuM4DOQy3HSS+aiLLp4LugS5ixPw4NFJzh0e4MnpWQTQiCIwSWXAsRN/0s2D/TRbdjin0nm1dc6Lc1V6QsH+I+u5WUve9pLdrM1XOLZY4H/fdxUPTo1zTjOLf6RCZofLvgPHsCyJZUukMUzOFRjtCTh8bA4/a2GlHXplSM6KWIg9PBSOVFhoBIJYCKbDDEo5uE7A/y2tQViGN+UPIwBbLFU5Ai2wROLHDLTCTpIKhW8s5pWHJzR1k4SgSAGRERyLe9AyS9GaYzby+NLiCE/7PcTaImVH1JspzunbxucOfZxGXGfgaovg0Ga258+jXB8ll5+hqSRC2QipcYhpmj7eMv5OtuR3nOVPuYsuvj+ks97pN2rheIu1OFLYjsXURIl8T1J5XN5ct2nbKEcPzDI3XaFW9jGAl7bpG8y/4K/jpGhLIdo+wu2qMQJk33FaYZO4S+C2CK+bPE7I5HFxBep/nuiOrU0QPwrELV9jWHKpMImGOX6idT0G9QyoEniXPKeq8fLXUWtIJqZqBEEJz3MYH7HJWUmoyehIkdJio1NBBvBiyaKrSEvRcpZoWWE6zqohGsv7Wdb2FE7QILdRC0K+u/8QYRxTSKUoplJUgoBQKYQAR0pUywvZGNMJ+cjYNo6V0JVIaTAGx7a4YsMGSo1mIgM0his3rutGRXfxguGsEORf+ZVf4fbbb8d1XTZv3sxf/uVf0tPTc8J2GzZsIJ/PY1kWtm3zwAMPnI3D6eI5or1clnZsSg0/STQSyfKVkEl3cH82w83veWOnc/mj37x7VbLc1jn/0s1fJMpKckpgP3UOn390A0+mfSIXBjIZzvE9MnVJ/6YhGs2QoBl2lnCVMXzj7gv4zz95L1XVoOBZjGYXyNhNfC0JtaQU2ww6EbaAWBkWZgvY/YpSZPFPRzdzJJ3iPw3uw1cWutVgKERiBOcKTYykHFsU7HiFPVxexjS0jSMNQtgERuCJGFdo/qG6mVTqQh6bn6KpAmICtqfnuMSuMu41qJkMD8RTBHEfrvAwXojetIeJcoNPf+t83ve6f8Z1FcJ1KNgOPXYvqZ7fRnpdctzFC4MflrH4eCcLL+UQhYqgubSE3m6u233/fsoLdSYOzqFijes52I4kjhSluSq7799/VirGJ2C593EbIgN6Dtm7ZK+ma59NyK/xW1VlG5BgrVl6nJ5K9MTxky0inSGpPLdbhC2WPJWXu2LYrdsr4Fz6vCzaGrVn2X9II6WF49hEkWL/oZAtG54l1wtvvfYSPvGpOwHIpF0azZAR5bDYF2N0wuGVNiij2dDXt2qIxvJ+lkYYsW1ogKdm5lY9nlqQyGRKjSYLjSYp2yZtOzSiCGNM4oNMEiWdcxMXpVhpqkFAHo+0Y1MNQuIo5t6DR0jZNoWUx/WXXciDRydPOhd10cVzxVnxQb7mmmt4/PHH2b17N1u3buX3f//3T7rtnXfeySOPPNIlxz9EWNNbpBFGjBcLaGPAmIQckwyUa3uLK3Rkp8uxv3zDWl6vB7nqoMfLS3mGQ5ctIsvOYy7bDthsmU+zOd/LjTdczc+88+X4foTl2ahYEWuN0lCZ3sI/fPmVaAnr89OJtlhJtJGMu036rIhQWdQiiziyGBhO0Z9bw2C6yavXHGFTapE+28eSilTbbcIoTJJGwrHIo2DHREYSt6o6oRHEwLDdpM8KGLYqDFpVatrjy+VzeaqZVL7W5IpgJC9J1XhH31HGUoKqKtKfLnNt4RCbvRpCCmwcjBEM7jjK7iPjfOJbr2Z+Zphxq8hI5sIWOf7Be5R28aOLH5ax+Hif4eHxXuJYYTkSo01i2dYI2XHJej710a9gjEnkFBKCIEqiis8dpXcgv6rH8VnBcu/jNkzjBOcImXsP9PwFeK8EazhxmLDXJpcA8REwZUCBCUA3kv93jCXFsr9EarUSDmAnlnHPAweOpkinYqyWvY9lSdKpmANHk2r+Reev48Ybrqa3J8NCqU5vT4Zf/4+v48I1I1gtuYRrW2wZ6Me1rZOGaFy+YS03v/2N3HbD9Xz55991Su9iSVL40MYQKYVlJTKOtOOgDeQ9jwvGRrlk7Rgb+3vx4xhJ4lDUnoskEMbJ+xjEilseePS0c1EXXTwXnJUK8mtfu6SfvOKKK7j11lvPxtN0cZbQXi7LuA4512nZ6hhyrsfG/l4cy6Ivm3lOOfardbK7seE3P/jGE6pBN95wNX/52e/w9JPHSEmL9T1ZPGV48olhLn1zL/vLLrHJsCF/AM8OsGUyoTRCg63A88CyJlFqmlCnyFsx1/UeRWAYtYNESiGW3DQDbeEKgyNMokVuTVCeXJqomlqCgEC7fKe+hn1hGtfS+KpBj5tmQyHH1ekHCLRA23X6vRS+Fghpc0VqgkNRL34UI4zETSku2boGWMNf3XchfU9m+NgN3Y7rLl54/LCMxcf//m3Hom8wR/9QkdJ8rdNct1yKoTXkCxm00tiORb4ng9HmlP7JLyieQxLdcr/hTkU5PkRCbmvtrUjIrw+kWKoct+UVKZKGvzbEsvvqoGvoqQuTaGt7G+RuSu46SfNdG1/7lwv5qdf9E2EkCCIPzwlwnYgv//OFnN+S6l50/jouOn9lXPVNedGZB9o2oI0w4qYztJ48lRVbkqSnWxacGtey2NjXSzGd4smpGap+yO5jUwgBadsBY8imPCKliZQi7dqkbIdQKS5aM8IjR6cI4phNA33AqeeiLro4U5z1JL3PfOYz/PiP//iq9wkheO1rX8ull17KJz/5yVPu55Of/CSXXXYZl112GbOzs2fjULtoYXnqUV82Q9Z1OWegn4vWjOJYVidpaLUkvpPl2Le7zXv6c5Tma/T0504aU3vR+ev4s/95PR/7rbexc8Mw1KPO9jtGI+qRi2dVcSy1woUi42jSaUXiQicBxajnYxQ0ApeiDLGEQUiDJUDQ1hpreq2QyAhSUmOLE+s6KalIS4Ug5hWZwyjjMOgOEGuLJ6v7OdacY8DxEZZH3kkjRdLO19SCXiuJpdVaJ/Zw8VIwywuZltdFF6fCCzEWP99xeLXf//t+52187HPv5c9v/a/8+p9ezwU7N61Iu/PSDlolTXqBn0gxTuef/ELi+STR6WBX4mksR0FkScixRmkHpUka0ZRGmwCwQI6BfQ5JrSpiRWLeCqf2Nt1sJhrn6CEo/SyUfgbC+xJXDHUQFn8BPXMNunRTcixALbqA2+58LXU/Rz5bo+7nuO3O11KLLjjl618+D8zVGvRlMyts3nYdPMJNt97BWz91CzfdescJ1VpHnpxeLLfX7M9meMloYi/64OEJZmoNAqWS0wNtaEYRtmUxks9x2brxJDbatgnimDBW3H94gorv48crbQdPNhd10cWZQpjjXdbPEK95zWuYmpo64faPfOQjvPnNb+78/4EHHuC2225DiBMXXCYmJhgfH2dmZoZrrrmGP//zP+eVr3zlaZ/7sssu60oyfoBYnpC0XNt10613nJDEVwtC+rKZs+ZBqUs3UWlMoOODSGJsCWnbT4jn8duaJDjE1zbPNvOclyuhSaYdKZbIr4GWrMJgixM9kTVLU1RsJFXl8LnqW/F1jX2VjaQtl5S9h3f33k9ORkiZwZE2vm4iTZOG8fj78vnUgyZGaMThC2D6XADqzZC+QreC3MXzx7/WWHw2xuGPvP+WTqhIdbHBoX3TQKJZHt8wgN8IT3pi/cMAXbopcYloN/aF96N1E60MQeTgOjFCJJXTRnw++ZFfbaXqPcKSh45gyTpOsKRNXg12QsRNSNuUEpECe2NS7c7/d3bvHen4HLc1xr4fceMNV59QNT5TLLdpW15dXk6gP/zVb/M3D+4+aSXZlgJt4JyBPjzHZt/sAn4cL42/ArKui0CgtEYKwbnDAxyYL1EPQgKlSDtJomul6aOBC8aG6c0kPvtney7q4kcfz1ti8a1vfeuU9//VX/0Vd9xxB9/+9rdXHZABxseTJKKhoSHe8pa3sGvXrjMiyF38YHGypKHVkvieyxLcmWL3/fv51Bf+mUd0hfO2pvnVfzfHmmKELdIgdKscoViKfk0gAFtCTsaMpeoYBMoIAiQ2Bk8sTTz2MoLdFl8s30+imSOxbpOGlC2ZrBvSlkvWTqFEk39prOfNhb2Exgey5KQEI/hOfT2hCUg7GWb2jCOnNpJJGRp+RDOMuml5XXxf+LcwFu++f3/iVbxKKMhyLJdi5Apphsd7mZlcJJ316OnPndLj+IcCxzf2iTRhFOLYEVZLsiWEwRjJdx5azxvfcjm6alrV5ihp4JPjoObATJzBExoQditQhFaTYJAQdA00Ps9F59/MjTdcfUJS3vMlx8AJ8rpIaY6Vq9z497dz5abESeJDb/gxAG599AmCeCXJFyQFjFdv2YhlW9yz/zCebWMwhHHiaAECP4rJeS6xNp0VzZlqjcWmj2dZeLaFMom7ktKaQwuLFNOpszYXdfHiwvOuIJ8KX//61/nABz7AXXfdxeDg4Krb1Ot1tNbk83nq9TrXXHMNH/rQh3j9619/2v13K8g/PDhZdfmFwu779/MHf347z6w1uFiIWLFj7CgffuM/0ZtR2FYu6RaPD4BZXPHY9je7Xf2tKwtHGpQRpKTquFW0fwCSJGtEY2GMWqE/aleRJYln8oLu4UhQYFf9lRyINqDEw0DAJrfMyzOHWOtpyirDM+ZifmL9H3b28+DeI3zhrkeZmCszPlDkHa+6sJuW18VZw9kciy+77DI+87/+/rTEt512l8q4K9LuTlYJ7pDpdvDHScj0DxM6QRzh9wAr8TG2ekGV8OtPgDA4loIWOf7/27v36LjK89D/33df5qbRXZYsS/JFlo0xxibBGJpynBs43AqhkEADK5xDQtKk55emJOX0stIkbVLaZJVFW9o00NCTHCiQQCgpUOIQEgwpxeAWGjDENr7qYt3vM7Nn9t7v7489Mx5Jo5sl2bL9fNZi2Z7Zs/c7W+LVo3ee93kGhssYTUZYseYTMPJXBDWRcxUrPDCawT8YlIXLvMpkGb2+tkikIkRDo6AUvi7BtiNgbwDtBxU3ah6f9/f7m/c9SE08hqEU/YkU+3p6MVD4WnP20iUTVpM/9+iTHOzp4+jwCCPpYCEjalmsW1rLg7d8NH++3Ue76E8EtaQVCj+798VQii0rG/OrwdvuuZ/RdAbHdYnYFg3lZWgNB/r6aaoon/Rn0UL/vBKnlwUJkFtaWnAch+rqaiDYHPIP//APtLe388lPfpKnn36a/fv3c+211wLgui4f+9jH+OM//uMZnV8C5DPH1z//ID8J9eCGDOxsdQnP9Th3ZTu/f8krLK+qBxXDS79MxnexlZv/YDKXP+Fp8FGM+hYjnkWplaHccIPOS5DdtAcWOrtyYQa1OAvyAXOBtAH4GDjmu+hM9mCS5KmhD7EvHUert/G0wjLCNJdU4fjOvNc03je8mx092+l1uqgO17K1ZpvUTBaTWsi5+Jyzz+UD6z81beBbmDaRkxhJUVEd54/vvmme3/HcTdV1btLjc404dAbcXwEqqJFshOnp3k/YThIOOaBA+wbJdJikE6d+SbbuMX42QCboqocRvJ4EuLspFiBrDU7GxvUswraDUpDO2GijmZLSpUE7aqMao/Jv5v0eFabXvdHRFVSTUJqQabKhvm5CesNv3vcgpqHY39uPaShMpXD9YLX4vt/6MA9kK1BkPI+3O7tJe35+Hrctk7rSOH925SVjAu7ZpvfNJC1EiEILUsVi3759RR9ftmwZTz/9NADNzc28/vrrC3F5cRo5eqSf1HpFpGDvimma/Nc7S7mv9oP82Qc6STjvkMiY9GfC1IYSlJqZ4MeJBpSJZcQZ8VKAR7Wdxsj2w8ulUqR1sFZsGcHHgC5xMjpBONtMxEeR9E1iRvDRn0UYyy6jWlt0jB7hwtjLvON8hJTXjFJHqA1bxO1yrpjn4HXf8G4eb3uAsBGmzKpgJDPI420PSGMRMamFnIsH+kbHNP8Y3w0vZ3wNZBjbFGQxma7rXFETGoqsCzrg+QfBeg+D3EhJ6mtYpoevDTxfYRkuSyqHwHeCRiJ6eFzTEAWRbWCfDQP/H+iJ9yrt2nR017Kksh9QuK5B71A5iZTmrJKRSStuzIfC9LpEtuOq72saqsqAiRvkGivL2XnwCFprkmkPT/sYKMKWxQOvvs75jfXc88LLZDyPULDLmrTnE7Yszqmv5XPv/bUxQezxpPfNpuqSECCd9MQi5js7+e3//SyfreymfaSMJ17fxC/bG/E8DzNmMaw3sd9q5u8O/iP1Zojryw/R50YpMXLNBxQZ3yRqeRhGCWEG6XNtSk0XW/lBHU0ffGUQNmwUGcBD61F8TDLaxlAenrYJZYNlRRSMoC1reSgGupHh9BF608Msizbx0eUf4d3VC/Nx8I6e7YSNMBEzqL+c+3NHz3YJkMUJl0m7+YoTOcUC36VNlRNWkE9kNYpZKdY9L5vLy2QB8vi8Y6MC7I3ZhiJ/w5pKSLXdieeC5yoM0yAcCmEZKcAH7QFhIJk7ARALqmFAUOUis4tjiV5BIO2kLeIxhwMdzbz0RlCv7aJzXqK8pAeMlcfXdY+ZraAXNgYxDQNTKZqrK/Mb5BLZLqs5N2/exM/27A9qHhtGsPFOa1zf4/XWDlr7B6kvjdObSJJIZwiZFre//0Juuej8omMsvH4uXeJz7506XSLXAKuQVLoQU5EAWSxKuZWcplUme/fHKC9L8JmLX+Ce59/Dq63LqKou5dJNcR5vewDHG2F3uopHtcH74+3EvSgVZgpPK1LaxKAa5bcz5McY1SGGMj4lhktjKEnYVIQJgQqBUUuP+iAdI09QaSbJ+Bblloej46SxWGJ0EVMEm2iyym1NeXgT33vP/waCVd6/3vNVWkf34+JiGSGaoqu4ov76OQexvU4XZVbFmMfCRpQ+p2tO5xXieNghi2TCmTbwLVYDPZVI8xuf+7UTOt4Zmax7njdFwwmzKVhpVgWr5OMaioRCcTCTRCMW+a55OkNQdYJjaRVBD7ngGD/bntqoB0IElS2CANnzbXoHYriu4vvPnEXf6FLKy2K8+U4DlRUx/vSPPozv7AyqaswwVQSCeXe0+6t0DngMJkzKY3uoq/gqJUu+XDRI3rKyKZ+6YJsmvtZFV3O3rGyiJGQznPLRaEyliIVC+L6mP5mkqbKceLiEZRXBCvSIk2ZXawe3TDHWyTaPT6axsnxCWkZhIC/pa2K8BclBXmiSg3z6KyyXNDSQ4HB7D66doC8d4//tvoXf2fbrvKEfYyQzyJ6BHobdZHbi1fhYVGqbD1f/iuZoBtOAaqMbrRSuthjx4yR0iBIjQ62VCn54mE20czEPdb5Gf7o76NakYLnVx/vjg1QYo3gqTL0dArM223K2E7x2UJVgn007F/NP7S8xkO7By5Zm0oClLCpD1dzYdNucJtz7D9zNSGYwv3IMkPISxO1ybl31+TnecSFmZ6Y5yHDqbLybUKYNps3lHZOWUdhQpKBmst//Oci8GcwZuARrUz6oiqDrXuZ1cjWQtTZIZCKEzSS+NsEoI2R6oJOAi9Ya39d4vsmRo+W0d1fyF/d/iKW1ZViWyfs/vIEu9wWuW/UwZWGHqK2xjDAY5VD2tSmD5N7Dn+Ro7yHSXgTTNPA8n5CZYmn1CqqXT97Jr3DzWzRkB2XsssFnbiPctnvupzeRzOcge1rj+Ron4/Ke5uUYBRVWfK052NvPuqW187ahbqoc5Krq4Xz6WtiI4vjJMXtIJHg+M0mALBYlv+faYCVHFdSSGLcr+5tv/xEkw7xztItUqYNvBJnFtuGxZ3AFtZEy/nx9C6XOn1NmJPMd8hSafr+CjNY0xTdjVP4NvrOTA91fpoQ+Bvw4PxspRymDX4u2U2WmGNHllJZ+hmWxlcHHj+7b4PeBUc+ojjOcacfzh3liqJG96TgGBkopdLYtasSM0VK6fk6BbGEOcrFJXIgTaUwVi0Ue+M7UTILdSV83RVqCP/LdbLUKi6C7XgZIBJ9G2S3g/ALw8DW4HiQzEeLhBJ5vkPEsQnYE2xgGfLQG11MYBjjpEP1DEf76ga18+INvs26ty9v9UZbFe6mL9uNqE883KAkb2MoF+1yM6gcmfR+H3/4AvSMxrGweMIDreVTHEyxf99y092+yIPSac9fxnf/4T3pGRvCzEYdtGtTGS8j4muWV5WNWdtsHhugYHuGs2pp53VA3WRWLqRYfttZsk3n3DCUpFmJxmsHHltXhWt5o3U/Is7GSBomIg7YyOK7NBfTzx+taqfQfJmmkSegwcSOJ1kGYXGYMkaAMYjfnfyiW0E2JkaHCHOZ/VrST9E36/DBDvs2KaDkx7yHgD4OAOrvSNOprjjptGBhoX/GeWCd70yX4+JiYaDQ+PglvhD1Dv2Tf8O7jnlRbStdzbcPN7OjZTp/TRVW4dt43AgoxGxsvaD6lA+LxjPAWfP5wXLA7fS5vYavpojK7wFoJ3tFgQx4A4WCO85eSLSCJ1pq0Z2MZflBdB0XGs4E0ViiILHMBpucHiwdl8TSfu3kXiaRJezJOZXiUpngXjmehMVFKk8xo7JAd/GI/hbb+MqriSdLusQA5Fs7Q1l/GTKomF9sIN5RyuGfHy5RFQtnibcEbMJVBz2iS6zat57W2oNFNLhjuGBqhvrx03jfUTZaWMVX6muz9OHNJgCwWp9jNwUqOz9iVnIJd2dsqlrGp8QfUhFL0OiW82NfI/lQ5azoqueLsF6m0miDjEzJMlJ9gxI8RURksMhgo0tHgB5/f/znQLhVmEk8rPG0SUmlKTZ8kpdgqTsyuCz5qzW3WyeYq9qUPYmBgKpMkIarN3EYbja99NLnNfQpDmXOuOtFSul4mZSEWUBAkcyxITjwQdOCcwYa3MSvJxILC6no0+LdaArjZpiBmNu84BahgD4S2SDgupgkZz2I0ESEeTjKcjlMV7cPXGkMF5d2UAiejMJTGtjySjo1WMVJpj5AVQwO26eG6QRtx35/ZB8Wvtb+PD637F5QFjhsmbDlYhsNr7Zczk4zxYhvhekcTuL6f73yX8TxcX5PxfSKWxb++8SvOqqtBAz0jCRory6kqibK0bGzlk4XcUFcdrp2wguz4SarCtbL34ww2ebN0IU4iI7wFSv8QjGrwe4I/C3P6nJ0s8x6iIhNi0IkQD6W4Zule1h6t5KLG3WiiQR6hEcVUFrYRIapcjnoVjOgyTKOMZd5D2dzAt8DvxVBhfBQ630dPU24MUW8OQfqVoBlJ5q3gKbMJdIK0n8bIpoGUGAb9XjT/HgqDY8uwWRJeStgIs6Nn+wm7j0KI2cmnWfi9Y0u9OTtn9jr3ALit4L4aNPnwUwSb7g5mo1sriHCVCoJlowoq/h6MapQRtC8ylAcY/NvuS+kcrKZvNIbrW7i+ia9N0q5BKhOiY6AMzzdIpizq68qpjjk0xY+g0FjKx1JptPaxTB9wwTxryvew5dzreODlSxlIxCgNDzOQiPHAy5ey5dzrZnTvGivLSaQzYx4bTaeJhWySGZewZVIaCVMSsrMBf9BcBCCZTaH4m+uv4qy6JRPOM74yxnzaWrMNx3dIeQm01qS8BI7vsLVmG9XhWhw/Oeb4XPAsTm+ygiwWrck+tvSdnTD4BfCHaaiKcKQ9QjJdiSod5aKNv6AqOoxrlDPqDlNiNIC3DxODmKFZHSoPapSaK/I//Dy/B61TONpEZX9nzBVTihouSvlACHQKSAfXz65wlxiQ9D0iyscyfH7lbSRiZHD8VLbasiJqllAbrqfEKkVrLSsPQixmx1PqLfc67YLfRrAynF1/8g8GZdf0AOCADnOsY96KYxUylCJsm3ieg225RGyHc5b+N4/+5/v4z0NX8771vVy78SkcN8RIysLQCcJWmq6+ZTSviBEvcYiFekhmfBwvRNhMEzIzKKUxjSgYlVA6dV3koKvnx3nk+U3H1e2zWH1i2zSpjkXpSyZJex6WClpIm0qhDFhSNUzFyoOUWyM8cOS/qaq+5bjqHM/FdOlrj7cFeduFOchX1GxbkLGIxUM26YlTyrFVmv1ABJSP6wU5csN2mnLLJaEqKLFSOJg0hMuI+EeBYYKQ1wj+NMrBbGTUNxlx9lBjDgcbWjBR+IQMA5N0cLwqJ/8DzWwEc2V+Y9/I0N8z4uxmxC/jLXcDh9zK/AaOHT3bpeqEOG2dbvNwPj3C+XkQFFtNQU1jmFHbZr/nWvA6gQzoEY59QOtlPwlzgJFg1VhFsuUiU+D3BxUqdFDnWOsknq9Ieya+tlHmMh54+VJ2HVzGuY1H+B+r/53qkj46hypoH2rh6ne1g/tLguoYITLaxPUydCXiVEWSRG2FFbl4RmXe5sP4jXDnN9bzxC/fxvU82oaGQWdXlW2b8qpB1q0/gqlC4Jtk/DQtdeVc23Azfb2li6YtdK6KRS54lioWZwZZQRanlsLVHZ0GZWGZUFM5hOnFSaty9mbWc6HxH4R1GsPrARUmKMTvEeweLwle6+5l1C0hoeMkdAYbD0t5ZLTJgFdCtamBCJAGFQ2CY1WWX/ExwlsoW7KFruHdvNiznb50F1XhoINec2iE2vhrRYNnWXkQYnEZW70iHnxa5O4NmnQYFRM2CBdlNgXpFSpCUOs41/7TDM5nNQRlIa2zjpWJzH2a5Q8RrDprlDKxTIVlusFr7Qo+esEbvLhnCb9sbeKdnptJpDIsr9zL723796BUnLkWvDeAFLZRim2vY0VJ5bHAfh7bTU9WCSKn2Ea4s5fW8sCrr5NyXVKuh681IctkTUtvNji28XyfsBnNp6Hduurzi6bDnez9ODNJvAp7hAAAO35JREFUgCwWvTEbX7wjYDYHwaq7N8iF0AYWDmEjymvp9Rz1l/Fy+iLeF/4ZWvtgRsFPZwvy+0AqWBXWEFODuKqcbn8puzPrWW/vplQN0++FqQ7VZuuWFvS5LvKDcvzkmfthGzdiqNBqyLSzxfh3LPO9nFX1v2SiFWKxKfzF22wEb18wt7hHwLJm1rbZPh+cZ7P1irPNQNDB33UiCI7DV4PuCeYxvz8Ijq1l4B0gWHH2COab3I/mFGR2Uxk2+dJHP8z9z5FPffidSw5QEq3I7rWIg64GPxls+DOzzVpmEtjPQmEZt5p4jL7RBH/xkx3Tll8bHzTnm4uEk+BH8Hwfz/dZVV1J2IhMm4YmdYnFiSABsljUxqzsGDXBDxn3V2CvC1Z3vFbQI2SI8ELqXfSxDICj/jIyvoEyQoR1EkiCjhJ8y2c/1sQkojwSpNmd2cxRfxlHnWWkvASrwyOs1a+BdoLX6FHw/xswwdqE7+xkfzpefJIu+GFbYkCJXQb+CJcbDoZM4kIsPoUd9MxKoCU/t2BUT1vqzXd2Bq2hjXrw2zkWHCuCgLcUzGWQeTFbzYIgkFZ1AGQ8UPjZvXsajYepClagMVlb/h3+4uN/iBG+Krhmz3dAFXT9MxvB3wP+aLAQUKTyz1wVK+OWe3w2q735VtFH/hvHHyFsRllVXUlFNErKS0y5Aa6wHnyZVcFIZnDO1YGEKEaqWIjFrXBlRxnBigsq+GhSlYG5EqxmhqJ/yIFMRX4XchUHiBgOEQOCNq0KGCX4GNPM/udiKMUv000czFSM2b38P2LdQcc8a22w25xUdkARUJAa+DI7j97FSGZwzCS9b3h3tqTT2FJH07arFUKcPNmqNMf+XQnWKgi/D6Pyb8Y2/ci2cPZ7rg3+zH3CpWJB0w/r3GywbRCkSJwH4fODNC2vM1idNmqC57x9tHYeZCBhkcyY+DqooKO0j+9D0IraD+Y9FQuuM9mYjYps3nRp0co/86G1f5BYyB7z2PGWX9uysonPnXcLLXXltNSWUh6JjKkeMZlcXWJPe7QmD9CWPER/uoenOx6d9RiEmIoEyGJxGx9smpVgtgDemB8CjZUf5dqGm4nb5Qy7A7wrfADDXIapsh91qhiQ24+a7ZKlSjCsNVxYGs+/Lm4HG0TiajA4xqwMPrJUpUFahvLBiDPgJtgcPkjEjKGUImLGjpVwG/+DC+b9o04hxDyK3Rz8P+qPZPN2R7KrrzePOWyyEnC4bx+bp8xKsDcAMSB8bKOf10rQRc8Nftm3VuL6mhK7g/aBEgylSLsmHQPBeYLOyypYZTYrJ/6SXWzMmFD+Vxg1j08I7HftOcId9z3JTXc+yB33PcmuPbP/hb1YGbe5lF/LVY8YP/9OtRLc63Th+i5HU624OoOlbHztcTjxTrBAsUjsG97N/Qfu5ptv/xH3H7h7UY1NzIykWIjFrVhHPSMM1nsmbDwpzAXOt6rWVdmPSpMEq8gKlM7m6QWb7uJ+z4SqEr5bcF2dJFiF9oJVIGDU15SbiWDPX1a+ePwMmpwIIRaPGXfQm6wEnN8T/D9eOE8pghSt9CvBvOGPAHZ2Ex9gVNA2sITK6FFCJrzdUUM87LC8ehDXM0j7BvFIDHQneKWg7DG/ZM+m69+uPUe467EdREM21WUx+oYS3PXYDm6/buuMS7hB8TJucy2/lpuzc+lquTrxkwXJ1eFa9g3vxlAGhsp1/FOEVGhButsV5juHjQgaTdp3psx9ljSQ04OsIIvFbYYrOxPkVnGNimA1J3RBsNtblQd/tzdMvTu98LpEgqoXuTJvQImhGPTGplHkisdP1+RECLH4GOEtwaprkdXXvMnSp4iMnae8juz+BRO0kS3xlgacbHm3QMJRvHqwiY/fdz2//d0Pc6Svgt3ttbzVsYT8j2dtgHeo6Lw3ozEDjzz/OtGQTUk0hFKKkmiIaMjmkedfn9U9yuUOV5XE6BlJUFUSm3aD3nRywWTRdLUittZsI+07aA1aazztobVPTWjpvNeYLxybicmRxH7aEgcwMaccZ2F76gmfMIpThqwgi0VtNqskYxRbxTXKg05W/si0K7tjrqu6gHSwyUYFG+4qrBg/TzSQ0glc36Un3Unad2iKNbNveDctpcWbnAghTmHFPtHSCbDPDuac3Dzl9wfVdlQ0aByiU7hehIyb5leHu0mkB6gp1URDGZ56fQsaeNfyNi5obsVQmlTaZiBRhq/TmEYKhclLbb/JT97ooq3nwRk18Ni15wiPPP86bT2DtHYPsrKucszzsYhNW8/x5Q7PZ/m1wmASyP852WpwS+l6mmLNdKZa8bRHyAhRGVqCZZjE7ep5G9f4sR1x9mMZFhroz/TQFGuedJzSnvr0IAGyWPQm66g33WsmBNbxbCA8w2C78LpjSs2ZTUQqfpctsThPdzxKe+owISPEsshyFMhHaUKcrqZInxozX+RSvJQBZiWDI0n2tHVRW9rHQCLG0vIBDveU8f1XNvGfh2p41/I2bv/QS3i+gVY+tuURCQ3SPliLYVTTOxzmyw8P0FQLdZVx+oYSPPXi91gZOUBlrDc7lx1rBDI+paKjd4h97T2saVhCeTxI8UikMjTULEzr5tk4nmDyyvqP5FMYFrK7XXviECkvSUanSfsOYSOCoUzSfnrKcVaHayc0iZL21KceCZDFaWvSwPo4VnaLnaslDPGeMpbHmsdMhDD56ocQ4lQXC0pNAljriqdPjVtpbu8dImQ6HOhewld+dAVJJ4PnH2tie8OWN0ikLY70lrO6th/PB89X1JX2MuxU8+irF2CaBv3DSZZWlXJu4xGuXP9jjnRZHPSjlMf2UFfxVUqWfBkjvGVMSgVAU20F+9p7Odw1wIaSOhKpDMl0hhtmkTs8fpFgvjrzHU8wOV1r6Pmwb3g3I+4QGo2lbDKkcfwUtgoTNsNTjnNrzTZpT30akABZiDmQj9KEODOMqcluvyu7ejw68ZjEA5B5C3R/kJZl1IEeJWJl+NFrm3AyLu9a3sb1m3/Jssoh2vvLaKnr5XBvOaB4p7uSZRXDROwMWvv8y39fxasHItiWQSpbQeKiFS8wlDQYdSzK4xbDKYNE5yhO253c/dz1tHYPUlMW43BXP6m0SyRkUVdRQu9Qkt6hxIxSNCZ97wXVO3zmvrfieIPJhe5ut6NnOxV2Nf2ZHjw8bBUmpZOktcNSuyFfkq7YOE9EAC8WngTIQsyBfJQmxBlisgoWiQcgvGVsEGmtJJEwwDnEQKKbnuFqfvDKZt5ob2Rj4wF+99J/J5G26BmOUVmSpKokiZMx6RwqZTARYSgZoSScYcSJs793NZFQJ8m0SzRbg7g82kvPcBTTNIJiGVozmDJZEu/GNBSJlMOBpIOpFLZtMjCapHc4QVksMuvKFTN573OxWIPJXqeLylANISNCf6abtEoTUzFcncHHQ2MRNiI83vZA0YoW0p761CcBsjhtLdRHgoXkozQhzhCF3fZyCmsTFwSRgyNJ9nfYxMK1JDNl/P3z13Cwsx/DcPnoBUE6xagTpD8knBAdA3HqK0YYccIk0jYVMR/bzPCvv7wQrTWV8Sgj3YNUlkbRWtPaF6eqJAWqBIBUxqUk5NLaX8rBo/1opUBrPK3x0i6GCqrOeZ5/XOXdpn3vs1RYOi1khFEoHD9VNNDMHdueOERaO9gqTENsxYzbS49vS90cW8v+xJ5p21TnFj/idilxuxSAlJcgbpfn531fewxnBulKtbNn+JdcVncd76u74rjuiVh8pMybOC1NVtDfd3bO63WOp9C9EOIUNF0DoIIScO29QxiGwvWjVJf0sbS6jJV1lURCNg2VQ4w6x7rRaaBzKE7faJT+0Rh15Q7aqKYt9dsk9bs43NnPYMKhvCTC4GiKw539PPPGZsqiHvFIBtBELIdoKMOjr2zEMBQKMBRjrhHLtoU+nvJu89n8aHzptLbEAY4k9hctnZY7tjvVQX+6j+HMIL3pTt4efJ2HDt83bfON8SXkulMdPNnxCF3J9mlLym2t2YbjO/nurIVd/nb0bMfXHn3pbjxcQkYYrTXPHH1MGoKcRmQFWZyeFvAjwfHkozQhzgDTNQAq2JiXSrvYtknYSnF0sJy3DgUpEoZSjKRrqSkdZihl4no+WmtioQz7u2vo4Wts23R+/pJV1UE1ippym1jEzm+ue/f6D/PPr0S47JxXqS0d4MBQjH/csYFftjUSCSkMQ+F7ABrLNNBaYxiKkGXOuLxbYZm4i9c2c/OF71ASneS9z8L40mmGYaEoXjrt6Y5H6U/3kPBG0GgMDBSKjM4wnOnnqY4f8LulX57yWr726HaO5qtRaDTd6aOEjEh+ZbjYpuqpUj8eb3uA4czgmGYlJhYZnZYN2qcRCZDF6WmePxIUQpzZpq3JXhBAR0ImhkpgqTT/7xfvJu16GEphGgb/tGMtX7z8PwjZJo4bIWw5hCyDf9t9MVdeXMsd9z1JW88gDTXl9A0nxlSjyP352v4Obnjvx3nk+U209QwSDdv0DI8StlO4vsYyTBzPxTQUvu8HAbOvqa8um1F5t/Fl4nYdXEb34Hv4nUsOUBnrmXk9+iIKNzanfQdTWSgUad8Bjm1y3je8m8OJd7CUhSao+OHjozBQaAxl0ZE8PObcP+98mue6nyThjhKzSnB9F9fPgAJXZ/LncXWGQ8m9kAQLG9uws/XrJ6Z2FEvFCBsRRrwhFApDGdgqDEo2aJ9uJEAWp6fJCvofx0eCQggBU9dkN8JbeLv1E6QGv0vM6qe1r5THdp3PG+1NKBRa+yyvreBXnav4m2dNbrn4V1SX9NE7WsVPXtvC4YFVE9pB7z7USUtDNSWE8tfJrQCfv7ZpTB7xrj1H+NaTL7H7YCeRkM3Syjjdg6OMptJEbJuVSyuxTXNG5d3Gl4kriYY43L+Gv/zxJr5x21VzuoeFG5tDRpiMzqCAkDG2dNqOnu2EjBA+eszrNT4KE8dPotHcf+ButtZsozVxkCc7HsFUJiEVxvFSpPwkBiZa+/ngeDyXDJ7v8vCR+7ix6TZaStdP2SoaYDDTDyg04GkfTyewVYiKUJVs0D6NSIAsTk/TfRwqhBDzKFh1HSQa+gixiE1n/wjvtPdimy6lsQjLqispj0do0uW8sr+eUe+84Li+YY72j+D6XYQti+W1Ffl20JGQTWv3IJWlx6rkTLYCfP7aJv7x9qYxqREXrV/Bec31vLa/g7aeQarKYjMq79bWM0h12dja7sfbeW+8wo3NlXYNR1NH0MCSSP2Y0mmPtz1ATWgpnU4bCgONnz+HjwcoYkYsH7z2pbsxlYllBPndFjb4yeyxU9NoBtP9PN3xKJ8r/ZMpu/sBlNsVGJj0pDvQgArWkjENi62yQfu0IQGyOC0dd4tqIYQ4DuNXXZdWldI9MApozl5xbFXRtkzOWVlHZWmMPUe66B9JsbSqlPbeITzfZ09bN2HbwvN8lKFIpV1Gk+kxOchTrQCPX1kG+NgHZ/deGmrK6RtK5N8LzF/nvfG5vQ2xVaS8BF3pdtBQH10OHFtprgs30u10kPCDewlgEOT+auBw4gC+9vBwMTCzgaqZvZrKv2Y6ae3wzuhb/MHrnyDlJ4kaMZaEl+XzlHPpExoosyqIRGJEzVhQAs53UCjZoH2aWbAA+Stf+Qr33XcfS5YsAeDP//zPueKKieVPnnnmGX73d38Xz/P45Cc/yR/8wR8s1JDEGeZ4WlQLcTqRefjEKbbq2rSknL1tPRMC3FyZtTvuezIfiHb2DzOaTONpjZN2iUdD+L7GMhRraw/wP1peoqFyiJJYM5V1a4C5pYsVrjTHwjYaSDpBEHxecz1P7XwbYMaB+WwUbmzOpTMsiyzPl8p8vO0BLqi8mFf6XyRshFlZsob+dA8DmV7SfpqwESbjZ/CVh1eQW+zj4fhJwkSzQfKxgNovWIGeiq99FIqkn6A9dZhlLCdul46pb59LEcmVgMuVf5Pg+PSyoCvIv/d7v8cXv/jFSZ/3PI/f+Z3f4Sc/+QmNjY1ccMEFXH311axfL99kQggxH2QePjGKrboWrhbnNt4VpjjkguqBkSTpjIung4BOA6OpNCHb4tINA9x4wc9oXFIPakmQKjbHLnaFm/Asw2D3oU5QitX1VfQNJXhq59tcuWVdPjVjtp33ZmOydIb9iT1jVppro8u4vul/sqNnO/uGd2ObNikvNSHw1WgcP0WIEApF3Cxj1BthpqvJHi5hI0rKT+LpDH2Z7mDTY0F9+/msfT/VZkBxcp3UFIudO3fS0tJCc3NQ2uXGG2/kiSeekIlZCCFOEJmH58cN793EXY/tACi6WlxMLqju6B0iZFs4GS8fwhnKIGQaXPOu1xhOmvNasrIwHeStQ13YlgVojvYNU19dRkfvEN9+6mUuPHv58XXem4VcVYuRzHA+XcE2wgxlBiaU0Nw3vJvhzCCj3kiw8bHIqrBGo/EwlMmvV13CweQ+0imHTLYD3nQ87RMxTSJESPnJ/OpwYXe/+er8N9VmQAmST74FDZDvuecevve977F582b+6q/+isrKyjHPt7W10dR07H+8xsZGXn755aLnuvfee7n33nsB6O7uXrhBCyHEaUTm4bkpTEWYaiX1/LVN3H7d1hkdmztv/3BQqSLtekTDFqZp4PuaSMgi7XqMpNLErE5SXsXYF8+gZGWuqsXe1h4A1jbW8NtX/Rrnr20akw6SSmewbRM0jKTSHOjoQ2XLw/UNJY6v894k4yl2b6rDtXQl2+nP9KCUgaksMr6Dq9P50mv7hnfzVMcPOJLYT8gIEyJEGqfodQwMomYJlaEa3lV1EUsSS3mq4/szCo6BfNCtlCJmxmkpXc+tqz4/5pj5qn0/1WZACZBPvjl10rvkkkvYsGHDhP+eeOIJPvOZz/DOO+/w2muvUV9fzxe+8IU5DfRTn/oUr776Kq+++mo+n04IIc50Mg8vnFwqQt9QIl967a7HdrBrT/Hg9Py1TXzjtqt48A9v4hu3XTVlcHzXYzsYSjjYtomvNaOpoCueRpNwMrieDxpa+8swSDA4kjx2gmlKVu7ac4Q/e+AnvHW4K8gsAHYf6uTPHnyWXXuO0FBTTmffcL6ByfCoQyrt5msmKxTRcIiSaOj4Ou9N8n6L3cetNdsYyPQCYGLmA9RKuyafTvF42wN0plqxlI3GR6vJr+XjsyS8lLARZkfPdvYn9tAUW8XK2BosZU/+wgJpz8H1XWJWyYJWpeh1uggb0TGPSS3lxWNOK8jPPvvsjI677bbbuOqqibUTGxoaOHLk2ETT2tpKQ0PDXIYkhBBnFJmHF06xesC5x+eyovrI86/jej4dvUMYhkFpNMRwMo3raVRh8KcUP37zfD5x8c840HGEcKiMxmqL0qg/ZcnKR55/PQi+TQPLNLKnUgyNOjzy/Ouc11zPi788gGkaREMWo6k0XsbHNBRag9Y+y6qDTxrmo7zbVPfxG7ddRdwqy3e6CxlhqsJLiJlx+pyu/Cqrpz3QioxO4+nJN9wZmJRYpWitx1SdUEphKYuIEWXEG5pyvK52WVWylivqr1/QldzCmtA5hZsBxcm1YCkWHR0d1NfXA/D444+zYcOGCcdccMEF7N27lwMHDtDQ0MDDDz/MP//zPy/UkIQQ4owi8/DcLFQ94LaeQfqGEhiGgWUqwMRUCk9rtAbbMojYNp7v8/O3qkk4F3PNea/RUNnDy3vK+PcD7+E3ttZz/trJz5/KuLieh9ZgGIqIbZHxdH7sTbUV9A8nSaUzlJVEcH0fx3ExDYPltRWUxyPA/JR3m+4+LoutmBAoprwEVeHafI6ygUlSj6KyDadzCRMGBkoZ+WYglgrCmmJVJ0JGGFdnphzrimgLPh6fW/snM35/x7vRrrAm9Hxs+BPza8EC5DvuuIPXXnsNpRQrV67k29/+NgDt7e188pOf5Omnn8ayLO655x4+9KEP4Xket956K+ecc85CDUkIIc4oMg/PzULVA26oKefg0T4i4WMf+Ws0tmnga4iGQlimIplI4/ual/bV8dK+DwFBxoRSiteOPMuXbrqk6Ep2NGyTdj1UNjjWPow6GaJhm4aactp6BqmrjLO0qvTY9bXmcNcAJZEQlmmgtZ638m7T3cepAsUdPdsZyQyO6YR3rLueAhQmJi4+Jia+9tg7/CamYXJZ+XU0xlaOaUzSkZo6d9syLOJ29Yzf21w22o2vCT2XDX9i/imt9cyqaC8imzdv5tVXXz3ZwxBCiDPWmTAPF5ZDm2llipme9/Z/+Fd8rQlZBp6vGU2lsUyDcMjG93wMw2A4kRpTmCxXqMxQUBaLsvmsxqKtn2+76/v8cn8Hrq8xFGgNvtZEwzZ//dlreOT51ycErKPJdL7T3kw3Gs7m/U53H3OrsLlAMbcKu294Nw8dvo/edBe5HG04VttYYbA82syIO0RfphsDk5gVJ26WYhpWvj107twhI8KB0T1FN+0pDBqiK2ZVReL+A3cXXf2O2+UTNveJU4t00hNCCCGKmG1litmc99NXXsi3n3oZJ+0RjdgsrYjTO5ykriJOxLY40j04oWpv7t++hmQ6PWmqR8LJcFZTLYc6+xl10qAUpdEQFSWR/NiLlaTLvbfcMbnKE3c9tmNGVTmK3ad9w7t53d7O2itaGegx6du7jLqy1RPOVayk2/0H7qYtcYjhzED+/SsMQOc33Hnaoz11GNsIsTTcSFV4Sb5kXCqT5HuH7uHjK/43t676fD4IL7PLGcj0ETSJJh90m8rkgsqLZ7WCm0sBKSQb7U4PEiALIYQQBWZa2m0uPvbB8zmrqXbMdc5rrs8359h8ViOvv9NG33ASz5/4QW/a9YiFi1dlyKU0bGpZln8st0IMMwv8C1d9CytPFFs9n+zYj13TwG4j2GS3tKSWymiSZQ2dXNtwKS2lk9/PwrQFx0+hlMJWdnYl3MVHk9ZBe+eoWUKFXUV/uofKUA0jmWE6nVaUMrAJkXQTEzrzLYusIOGOktYOmqB6hmXYaK15rvtJGmMrZxwky0a705cEyEIIIUTWbALDuSpcrc352AeP/f2O+57krUOdtPVOrLqgNeza28Ztd30/X984N/6+bH3lSMimaUk5tmVOyCUudu1Cs6ngMdmxz3b8G6uaYrOu81tYHzjjO5jKwsAHpUh6x1pLR4wooOl1utBK4/hJ+jPd2XrKJj4eESNK2AjzXPeT1ITq8mMwlEFUxVDKwNcehjLwtc9wZpB/eOcvWR5bPaMqFrLR7vQlAbIQQgiRNZ+l3aZq1jHZ8eNXlNt6BunoG6LIIjJwrL7xl266BCAf3Lc0VNPaPcjeth7OWVk36wB/NhU8JjvWMQcJG2M3vM0k/aAwbSFkhMnoTLART2cwlYmnNYYyAUXad3C1h6kNhtwBUn4SmxA+Hr72qQovIWxESbijhCPHag6HjDAZP0PKSxAxo2gNjk6hMLCUxdFUa36zHTBplQrZaHf6kgBZCCGEyCoW7KVdl5ffOsxNdz4445SLXLOO7qFEUItYjw1mZ5Km8P0d/03YNicEx7nNegC2ZeXrGwP54L6EEJWlMUaTaSpLYxPyiqdLH5lNBY/Jji31ynH85KzTDwrTFirtJXQ6rbj42CqE67vBPdAGKZ0gyCMOqlloDbYKkfHTRIwoVeEllFilpLwEMaskP5ZRdzgIjv0EGo3v+2R0GoCQEcJUFq7OEDbCPNXxA9K+M2WVivnqrCcWFwmQhRBCiKzxwd7ASJJ3OvoIW1Y+cP2zB5+lpryEpJOZkDucCzqLNevY1NTKte9+nSX+/8Pv3wCxmzHCW9i15wh//E/PMJxIURIJs6y6DIDugRHCtkVpLMxI0iFXcyoXHJsGmKYinXbzK7tTrfrOJn3khvdumnQj33iTHXtt/eXs9rcDx9IPhtwBNPDNt/9o0prBhWkLJVacSr+GgUwvETOKYRiY2mLIGwCtMFTwy4dSiiG3H0vZlIcqsVWIXqeL9uRhTMPk/PJf52ByH0kvQa/TFTQOwcbDxdEpAEIqgmXYpD0HH5+25CFSfjKbyhGsOlfaS/Jd+iQoPr3NqdW0EEIIcTq54b2bSKYzjCbTaK050jUAWrO8NujG5no+3QMj7G/vpbosxqGjfdzzxL9zqLN/TNC550gXGS/oTgdwbsMRPvuBHZRHR+keioLfC8N38vY7T3LXYzsYTjiEQxZp12N/Ry+HuvpRwHDSwXW97CrpMQqIhcN4nsa2LGJhm96hBLv2tPHWoU4Gsq2pC1d9C9NHlFJTtpLObeSrKovRO5Sgqiw2aZrGZMdeuf59XNtwM3G7nGF3gNwqr4Ixq7H7hnePOV8ubSH3uqhVwrLoCsJmlOpQHaZpYmAQM0qC2sd4mFjYhHD9DGnPocfpxNEpwmaUCquaXw69Sneqg/bUYRydQqOpCi0hYsayxeM0oEl7DhmdxsTC94NScI6fynbxywSr2b47aZrIzzuf5k/e+CxffO0W/uSNz/Lzzqdn+q0nFhlZQRZCCCGyxld48HxNy7KafGe59t4hLNPE9XyUUvSPJDFNg/7hJEurSvMrz71DCWwzqHFsmYqr3/UaSccmmQlTUWKDEQcfUoPfJRr6CCWRoLmHZRq4GAwnHEBjKINIyEJrSGXc/DhtK1jfyrgupbEI3QMjlJdEGE06JNMu+zv6WFZVimWZ+VXf2XYGnG4j30yOLUw/uP/A3YCe0aa93OvGN+Jw/GSQSmHYpP0UrnbRaDI6TUY7AIy4w9iGzdJII33pbrqcDjxcVLYPn0bj+A69mS5CRoiIiuLoFI52MFCEVBjTMEh5DgoDBaS1g+ErPO3RmjzAipI1E97rzzuf5smORzCVSUiFcbwUT3Y8AsD76q6Y0X0Ui4cEyEIIIUSBwmDvjvuepG8okX8ulc6gsq2bg3+72JZBKn2shXEsYhMJWURsk+6hBBpYWjZIz3CUkG3kUyhQMeKhVmIRm/rqMg509OHiYxgKX2uUUkTCFq6nyXgehgqqL4TDFslUBtPwWL+iLki50ORXhNt7hxhNOQwmHL7+vy7Lv5eF6gw4U4Wb73K1itO+g0oq9g3vLpqyUFjRAo4F1SEjRLdzFAiykHONP0IqTFqn8XyX9tRhLGXhZZ/T6GzAq9B4uNoloqJoA0pVOSVmKZ1OOwCWsrGUi9aQIQi8g1xwhYfPsDswYczPdT+JqYKScQAWNvjB4xIgn3okxUIIIcRpYdeeI9xx35PcdOeD3HHfk+zaM3Vb4ZkYn3KRWz2uzwa5kZBFxvWJhI7VJE6kMiwpL2FJRRy0JpnK0N5fxpIyWNtQQ3k8yGkdHu2jra+UXXva6OgdIh4NkUpnGBwJcmJNIwiIU5ljwbdtGby7pYHzVi/j1zes4r7bP0rSyRCLBNcvj0c4e0Ut569tpLosNmZVd/x7GU2m56WV9ExVh2tx/GS+VnFGZ4JVXWUWTbWAIKgOG9Exj4WNKP2ZHmpCdZjKypd9UxjZdIsgtPF8N1vtonCXo0bj5/+edEfxfJdKewmVoRrCRpjG2EqaYs1YysYlPW5EOr+avaNn+5hnEu4o5rh1RxOLhDs621slFgEJkIUQQpzychvQ+oYSY3KB5xokj8+vXb2siiVlMWzTRGtNZTyK5/lUlkbzQWfP0CjdAyNoDeevbWRjcz2/OPDrNFSHKI95oH2GR3roGezhuT0XYqgg17hzYBRTGcTCNvFICEMZKAWepzGUIhKyKYkEq7+FqRENNeUkUpkx486tDBf+0vDI869z5ZZ1M8orXghba7bh+A6dTitpP03KS+L4KUqtsvzGt/FyQXUhx0+ChspQDQ2RlfnUCUWQAmGoIEj18Ei4I/nXBekVxzrnQRA6a61RKjhvfXQ5ju+Q8hJjjguuYeb/LFauLmaV4OGOeczDJWaVHO8tEyeRpFgIIYQ45c1n/eLxxufXFpZKW7G0imvec86YKha5OmyFYzncv4YHXi7h0x/YD94RDnRb/PjNKxhIn8XqZSnePtIFWuNpzZplS9Bo9rX3olBUxqOkMi4K8ivXhakRk1WROK+5nrse24HrevSNJDl4tJ+X3zrMp6+8kG/cdtWc7snxaCldzwWVF/Mv7Q8EHeyUiaUsBjJ9k9ZHnqwRRxDIJonbpUScGI4fbLwzlUWlXUN3ur1g813Az64cBykWQQe9kBkGrel2jlIZqsnXPX6649F8GbgcpRQhImj8ouXqPrDkqiDn2A9Wjj1cPO3xgSUn/l6LuZMAWQghxClvthvQ5mK6Dng33flg0bG8uGcJn7nu8wB8877gGKWCtIiQZRKL2Liun98QuLq+ikOdA5SVREm7Hksr49mNeGNTIyZrHf3I86/juh4dfcMYhiISMkm7Pt9+6mXOaqqdNOg/3vbaMznH/sQeYmYJPhpTmQD42qMn3Vk0B3myRhxAPnCuDddzNHUEDdRHmuhJH8XEwjCCph8GJklvNJ+LbCqLKruWqBkL8qC1g6+9fG3jfcO7cfxUME6t8bRLRqexsFEqSAkp1i0vl2f8XPeTJNxRYlYJH1hyleQfn6IkQBZCCHHKO9kb0GY7lvHHREIWybSLZRq8daiLVDqDZZqsbazhvts/Om3wWSxov+uxHfSNJDEMla/FHLJNUk5mzMr6VPWRgRkFzjOtsdzrdFETWkqn04ZHNu1BQ0Y7bJ2kPfNkjTgKA+eG2CoUirSfwsenIboCpRR96WAjYMyMEzGjLIutyDchAYjbQSORuF2ev0ZuY2BNqJ5OpxXTsMDTuDqDgcHy6IpJ21C/r+4KCYhPExIgCyGEOOXNprHFYhjL+GMq41EGuwbIuB6+rVGGwnFdDnf1c9td3yeRbUoym5zhhppyDh7tJxIy8495niYWDo1ZWZ8sPeVbT75E0snMqLHITFNcwkaEo6lWPN9DKxcDA9uwaYo2z7rxxmSB8/0H7s4HwSVWKQApL+i6N5wZ5EhiPyEjTIlZyqg3RNpPszy2Ol+VIldtI2IqoJH+TDfa8DGx+GTzF6RByBlCNukJIYQ45c2mscWJHMvhzn5aewYZTaV55PnX85sGx493xdIqVtZVEAvbaK0JWyZLK+OMpDK83dpNV/8Iv3jjALf/w7/yzz/dNeZ6k1XvuOG9m7BMg7TrowHXC9oqV5XFxqxmt/UM5qtg5MQiNntbe2bcWGSycxQG4vuGdzOY6cf101iGja2C42NWnCvrP3L8N3yc3GbAlJdAa03KSzDkDjCYCZqvLIssx/UzdKU78LTHsshyQOcraRRuDIzbpTTFmmmIrmBt2bkzDo73De/m/gN38823/4j7D9xdtEKHWNxkBVkIIcRpYTaNLRZabhx3PbaDmnKbWMSesAI7frw33fkgG1YtRamga95bhzpRaJIpFzNqEAnbpDPemBzi6VIbPn3lhXz7qZcZTTiggo1mR/uGuebXjgV6k6WEANMGvdOdozAQ39GznXK7ghKzNFsDOY1t2JRaFXNald03vJsdPdvpdbry7avH5y1rgr2TETMGJoQzEZSvCBlh4nZZ/lxPdfwAheJwYj8hI0RNaCmWYRXNOZ5qPIXNTXIdA3M5zuLUICvIQgghxAKYTWtnmFiuLZV2cTJeNoc4aDYdsgw8z8+fY/w1Mp5HR+8Qt//Dv3LHfU9yVlMtn77yQsIhG9MwiEfCLK0q5amdb49ZaS5WH3ltY82k5ePGm0mN5VxN49yq7Or4OlbE1pDxneO+x7lgdCQzOCYYBbh11ef54ro/59ZVnyftO2PqKad9BxOLdMG1Xd/lSGI/oLOryor21GE0zCq4LWxuopQiYsYmLWMnFi9ZQRZCCCHmUW5D3Yu/PEBJJERDTXm+MsVUlTXG5yVbpoHva+LREGnXw8m4eF7QunrPkaAkWlvPIKahOHyon5GkQ8bzCdtBvd7canI0bLOmoWbM6u7R3iH++J+eoTqbbnHllnVjStXlAtuZ5nVPVkmjcIW8Olw7ZoMcULRc2mxM1mlvfPvq8dcOGWHSvkPICOeP6UkfJZQ7lwlxu2zCBr6ZKOwYmDNZGTuxeEmALIQQQsyTwpSHkmiIVNplf0cvzfXVlMcjU1bWGB9kNi+r5u3DXaRdF9fz8bVGa0i7mva+Yf75p7uIhm3eOtyFbRp4vgYNqYxHPBrOB8R7W3vYtLo+f52BkSTtfcP4vs/axhr6hhI8tfPtojnb0wW948c/VYrLZDWNZ5q6UMxMg9Hx1y4xS0l6CeJWGVprHD9J2k9nV46nPtd0FuIXAXHiSYAshBBCzJPClIdl1WUc6OhDE6z0WqYxbWWN8UHmP/90F3f/8EV8v7BlBXiez10/fAHLULiexghb+Dp7hA7ybSGbQ6ygs3+E/uEkqXSGtOthGoqSSDif+pEb+0LmcE9W03guebkzDUbHX7s2uoyLqt/H/sSe/FiWx1Yzti318QW2C/GLgDjxJEAWQggh5klhw5KKeJRV9VW09w4xmkxTVRabdQOOj33wfL6/479p6xnE8zWKYKOdrzXa17gaIrZJKu2hNRiGIhay8byga1wilaG+spTDXQOYpoFtGSScNBn3WFc+KJ76MdPaxrMxWWm24zWbYLTYtd/HsZrFuXzmmZxrKgvxi4A48SRAFkIIIebJ+GoOFfEotmlSVRY77vbOaxuX0No9iGkYGApc388/52uNZZlYlgVa42f/i9hWfqNcPBamaUk5/SNJUtlmJKYyGBp1aKgJzlMs9WMh23fPl/kMRuf7XBIQn9okQBZCCCHmyXw3LNm15wh9w4kgfUJrtIJcJoVBkBDg+xplgO/7LKsu42j/CCXRUH7F+q7HdlBXVcrS7IrxwEiS/R19jKYctNaTjvFEtu+ei/kMRiWwFTkSIAshhBDzZCbVHMabrI10YYpDNNuK2s8GxwpAKUojNiuWVnGkawBMgxVLq7jjhvePuV6xVe1lVaUMJhx6hxKTjnExte8W4kSTAFkIIYSYR7NpWDJVnm9hisOahiXsaesm43r5FWTLNFheV4ltmtRXl02aG1xsVduyTL7+vy6bcpyLoX13sSYgssIrTgSltdbTH7a4bN68mVdfffVkD0MIIc5YMg/Pjzvue3LCKm1uQ18uxSHXWW9wJEVbtm31OSvr0EDSycxplXo6x/u64zE+GG6OreWV/hcJG+Exm+akI504EWQFWQghhDhJpsrzHZ/iUB6PYJnGcW34O9423CeqfXex9szPHH2MylDNtE1AhFgICxIg33DDDfzqV78CYGBggIqKCl577bUJx61cuZLS0lJMM9iBK6sRQggxf2QuXvymyvNdDCkOJ0qxjngeHsOZQSpDNfnjpCOdOFEWJEB+5JFH8n//whe+QHn55An9P/vZz6ipqZn0eSGEEMdH5uLFLVehYvehTiIhm6Yl5diWmQ+Cj2fD36lqso54jpcc85h0pBMnyoKmWGit+f73v89zzz23kJcRQggxBZmLF5/CzXktDdW0dg+yt62Hc1bWjdlsd6JSHE62Yh3x4mYprk6T8hLSkU6ccMZCnvyFF16grq6ONWvWFH1eKcW2bds4//zzuffee6c817333svmzZvZvHkz3d3dCzFcIYQ4Lc3XXCzz8PwprFBRWRrj3OZ61q+oo7I0dkYExONtrdmG4zukvARaa1JeAtOwuKzuOuJ2OcPuAHG7XDboiRPmuKtYXHLJJRw9enTC41//+te55pprAPjMZz5DS0sLX/jCF4qeo62tjYaGBrq6urj00kv527/9W7Zu3TrttWX3tBBCBE7WXCzz8NzcdOeDYypUQLDS3zuU4ME/vOkkjuzkyVWxyHWxk5Ju4mRasDJvruvS0NDArl27aGxsnPb4r3zlK8Tjcb74xS9Oe6xMzEIIMTMLNRfLPDw3xcq7He0dYjDhUF0Wm3O+8YkszybE6WjBUiyeffZZ1q1bN+mEPDo6yvDwcP7v27dvZ8OGDQs1HCGEOCPJXLw43fDeTSTTGUaTabTWHO0d4kj3IOUlkTENQ3btOTLrc+fym/uGEnM+lxBnqgULkB9++GF+67d+a8xj7e3tXHHFFQB0dnZy8cUXs2nTJrZs2cKVV17JZZddtlDDEUKIM5LMxYtTrkJFVVmM3qEEgwmHptoKllaVopSiJBoiGrJ55PnXZ33uwvzmuZ5LiDOVdNITQggxazIPz6/5zEmW/GYh5m5Bq1gIIYQQYnoNNeUkUpkxj+UahpzMcwlxppIAWQghhDjJxuckjybTx901bz7PJcSZSgJkIYQQ4iQbn5NcVRYb0zDkZJ1LiDPVgnbSE0IIIcSJd6Z04BNiocgKshBCCHGSSWk2IRYXCZCFEEKIk0xKswmxuEiALIQQQpxkbT2DxCL2mMdiEZu2nsGTNCIhzmwSIAshhBAnmZRmE2JxkQBZCCGEOMmkNJsQi4sEyEIIIcRJJqXZhFhcpMybEEIIsQhIaTYhFg9ZQRZCCCGEEKKABMhCCCGEEEIUkABZCCGEEEKIAhIgCyGEEEIIUUACZCGEEEIIIQpIgCyEEEIIIUQBCZCFEEIIIYQoIAGyEEIIIYQQBSRAFkIIIYQQooAEyEIIIYQQQhSQAFkIIYQQQogCEiALIYQQQghRQAJkIYQQQgghCkiALIQQQgghRAEJkIUQQgghhCggAbIQQgghhBAF5hQg/+AHP+Ccc87BMAxeffXVMc/deeedtLS0cNZZZ/HjH/+46OsPHDjAhRdeSEtLCzfccAPpdHouwxFCiDOSzMVCCDG/5hQgb9iwgR/+8Ids3bp1zOO7d+/m4Ycf5s033+SZZ57hs5/9LJ7nTXj9//k//4ff+73fY9++fVRWVvKd73xnLsMRQogzkszFQggxv+YUIJ999tmcddZZEx5/4oknuPHGGwmHw6xatYqWlhZ27tw55hitNc899xzXX389ALfccgv/8i//MpfhCCHEGUnmYiGEmF8LkoPc1tZGU1NT/t+NjY20tbWNOaa3t5eKigosy5r0GCGEEMdP5mIhhDg+1nQHXHLJJRw9enTC41//+te55pprFmRQxdx7773ce++9ALzxxhts3rz5hF17Jrq7u1myZMnJHsYYi21Mi208IGOaKRnTzCSTSd58880FOfdimItlHp49GdPMyJhmRsY0M/MxF08bID/77LOzPmlDQwNHjhzJ/7u1tZWGhoYxx1RXVzMwMIDruliWVfSYQp/61Kf41Kc+BcDmzZsnbEQ52WRM01ts4wEZ00zJmGZmIQPGxTAXyzw8ezKmmZExzYyMaWbmYy5ekBSLq6++mocffhjHcThw4AB79+5ly5YtY45RSvH+97+fRx99FIDvfve7J3RFWgghTncyFwshxPGZU4D8+OOP09jYyEsvvcSVV17Jhz70IQDOOeccPvrRj7J+/Xouu+wy/u7v/g7TNAG44ooraG9vB+Av//Ivueuuu2hpaaG3t5dPfOITc3w7Qghx5pG5WAgh5pk+BX37298+2UOYQMY0vcU2Hq1lTDMlY5qZxTimhbIY36uMaWZkTDMjY5qZ03VMSmutT3aQLoQQQgghxGIhraaFEEIIIYQosGgD5MXeOvWGG27gvPPO47zzzmPlypWcd955RY9buXIl5557Luedd96C7nD/yle+QkNDQ35MTz/9dNHjnnnmGc466yxaWlr4i7/4iwUbD8Dv//7vs27dOjZu3Mi1117LwMBA0eNOxD2a7n07jsMNN9xAS0sLF154IQcPHlyQceQcOXKE97///axfv55zzjmHv/7rv55wzM9//nPKy8vzX9M//dM/XdAxwfRfC601n/vc52hpaWHjxo3853/+54KO51e/+lX+/Z933nmUlZVx9913jznmRNynW2+9ldraWjZs2JB/rK+vj0svvZQ1a9Zw6aWX0t/fX/S13/3ud1mzZg1r1qzhu9/97ryPbSHJPDx7MhdPTebimVlMc/FimYfhBM/Fc07SWCC7d+/Wb7/9tn7ve9+rX3nllfzjb775pt64caNOpVJ6//79urm5WbuuO+H1H/nIR/RDDz2ktdb605/+tP77v//7BRvr7bffrr/61a8WfW7FihW6u7t7wa6d8+Uvf1l/85vfnPIY13V1c3Ozfuedd7TjOHrjxo36zTffXLAx/fjHP9aZTEZrrfUdd9yh77jjjqLHLfQ9msn7/ru/+zv96U9/Wmut9UMPPaQ/+tGPLth4tNa6vb1d79q1S2ut9dDQkF6zZs2EMf3sZz/TV1555YKOY7zpvhZPPfWUvuyyy7Tv+/qll17SW7ZsOWFjc11X19XV6YMHD455/ETcp+eff17v2rVLn3POOfnHfv/3f1/feeedWmut77zzzqLf3729vXrVqlW6t7dX9/X16VWrVum+vr4FHet8knl49mQunpzMxTO3WOfikzkPa31i5+JFu4J8qrRO1Vrz/e9/n9/6rd9akPPPp507d9LS0kJzczOhUIgbb7yRJ554YsGut23btnx3rosuuojW1tYFu9ZUZvK+n3jiCW655RYArr/+en7605+iFzA9v76+nne/+90AlJaWcvbZZ58S3cueeOIJPv7xj6OU4qKLLmJgYICOjo4Tcu2f/vSnrF69mhUrVpyQ6xXaunUrVVVVYx4r/J6ZbI758Y9/zKWXXkpVVRWVlZVceumlPPPMMydiyPNC5uGFIXOxzMVzdbLm4pM5D8OJnYsXbYA8mcXWOvWFF16grq6ONWvWFH1eKcW2bds4//zz8x2oFso999zDxo0bufXWW4t+xDCTe7dQ7r//fi6//PKizy30PZrJ+y48xrIsysvL6e3tnfexFHPw4EH+67/+iwsvvHDCcy+99BKbNm3i8ssvX7AObYWm+1qczO+hhx9+eNIA6ETfJ4DOzk7q6+sBWLp0KZ2dnROOOZn3ayHJPDw1mYuLk7l45hbrXLzY5mFYuLl42k56C2kxtE6dykzG99BDD025avHiiy/S0NBAV1cXl156KevWrWPr1q3zPp7PfOYzfOlLX0IpxZe+9CW+8IUvcP/99x/XdeZrTLl79PWvfx3LsrjpppuKnmM+79GpZmRkhOuuu467776bsrKyMc+9+93v5tChQ8TjcZ5++mk+/OEPs3fv3gUdz2L9WqTTaX70ox9x5513TnjuZNyn8ZRSKKVO6DXni8zD8zsmmYtPTTIXT2+xz8Mwv3PxSQ2QF0Pr1LmMz3VdfvjDH7Jr164pxwtQW1vLtddey86dO4/7m3ym9+u2227jqquuKjqW6e7dfI/p//7f/8uTTz7JT3/600m/aefzHk12/uned+6YxsZGXNdlcHCQ6urqeRtDMZlMhuuuu46bbrqJ3/zN35zwfOEkfcUVV/DZz36Wnp4eampqFmxM030tFuJ7aCb+7d/+jXe/+93U1dVNeO5k3CeAuro6Ojo6qK+vp6Ojg9ra2gnHNDQ08POf/zz/79bWVt73vvct6LhmS+bh2ZO5+PjIXDxzi3EuXozzMCzcXHzKpVgsptapzz77LOvWraOxsbHo86OjowwPD+f/vn379jE7L+dTYe7R448/XvQ6F1xwAXv37uXAgQOk02kefvhhrr766gUZDwS7lb/xjW/wox/9iFgsVvSYE3GPZvK+r7766vyu1kcffZQPfOADC7oiqLXmE5/4BGeffTa333570WOOHj2az73buXMnvu8v6A+KmXwtrr76ar73ve+hteY//uM/KC8vz3+0tZCmWiE80fcpp/B7ZrI55kMf+hDbt2+nv7+f/v5+tm/fnu9ydyqTeXhyMhdPTubimVmsc/FinIdhAefiue4oXCg//OEPdUNDgw6FQrq2tlZv27Yt/9zXvvY13dzcrNeuXauffvrp/OOXX365bmtr01pr/c477+gLLrhAr169Wl9//fU6lUrN+xhvueUW/a1vfWvMY21tbfryyy/Pj2Hjxo1648aNev369fprX/vavI8h5+abb9YbNmzQ5557rv6N3/gN3d7ePmE8Wgc7X9esWaObm5sXdDxaa7169Wrd2NioN23apDdt2pTfmXwy7lGx9/2lL31JP/HEE1prrZPJpL7++uv16tWr9QUXXKDfeeedBRlHzgsvvKABfe655+bvz1NPPaW/9a1v5b+n/vZv/1avX79eb9y4UV944YX6F7/4xYKOabKvReGYfN/Xn/3sZ3Vzc7PesGHDmMoGC2VkZERXVVXpgYGB/GMn+j7deOONeunSpdqyLN3Q0KD/8R//Uff09OgPfOADuqWlRX/wgx/Uvb29WmutX3nlFf2JT3wi/9rvfOc7evXq1Xr16tX6/vvvn/exLSSZh2dP5uKpyVw8vcU4Fy+GeVjrEzsXSyc9IYQQQgghCpxyKRZCCCGEEEIsJAmQhRBCCCGEKCABshBCCCGEEAUkQBZCCCGEEKKABMhCCCGEEEIUkABZCCGEEEKIAhIgCyGEEEIIUUACZCGEEEIIIQr8/x3cUOpPVVJzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_data_sets_visualization = 10\n",
    "colors = cm.viridis(np.linspace(0, 1, n_data_sets_visualization))\n",
    "indices = list(range(n_data_sets_visualization))\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 6))\n",
    "for idx, c in zip(indices, colors):\n",
    "    ax1.scatter(x[idx, :, 0], x[idx, :, 1], color=c, alpha=0.7)\n",
    "    ax2.scatter(x_obs[idx, :, 0], x_obs[idx, :, 1], color=c, alpha=0.7)\n",
    "\n",
    "for ax in (ax1, ax2):\n",
    "    ax.set_xlim(-10, 10)\n",
    "    ax.set_ylim(-10, 10)\n",
    "    ax.set_aspect(\"equal\")\n",
    "    sns.despine()\n",
    "\n",
    "ax1.set_title(\"data from well-specified model\")\n",
    "ax2.set_title(\"observed data\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01bd544d",
   "metadata": {},
   "source": [
    "### Detecting misspecification in summary space\n",
    "\n",
    "As proposed in our paper [2], we will detect the deviating observed data as deviations in the structured summary space. Therefore, we compute the learned summary statistics of the well-specified data $h_{\\psi}(x)$ and for the observed data $h_{\\psi}(x_{\\text{obs}})$ by a simple pass through the trainer's summary network $h_{\\psi}$.\n",
    "\n",
    "\n",
    "[2] Schmitt et al (2021): https://arxiv.org/abs/2112.08866"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "85062ee3",
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_statistics = trainer.amortizer.summary_net(x)\n",
    "summary_statistics_obs = trainer.amortizer.summary_net(x_obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ef3cbcfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
    "colors = cm.viridis(np.linspace(0.1, 0.9, 2))\n",
    "ax.scatter(\n",
    "    summary_statistics_obs[:, 0], summary_statistics_obs[:, 1], color=colors[0], label=r\"Observed: $h_{\\psi}(x_{obs})$\"\n",
    ")\n",
    "ax.scatter(summary_statistics[:, 0], summary_statistics[:, 1], color=colors[1], label=r\"Well-specified: $h_{\\psi}(x)$\")\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.2)\n",
    "plt.gca().set_aspect(\"equal\")\n",
    "sns.despine(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86bea4e7",
   "metadata": {},
   "source": [
    "The summary space shows a regular pattern and does not fail in an arbitrary way.This visual discrepancy can be quantified in many ways. In this case, we choose the *Maximum Mean Discrepancy*, more specifically its biased estimator [3], as implemented in `bayesflow.computational_utilities.maximum_mean_discrepancy`.\n",
    "The larger the MMD, the more do the samples deviate.\n",
    "\n",
    "[3] Gretton (2012): https://www.jmlr.org/papers/volume13/gretton12a/gretton12a.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "45c18862",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated MMD in summary space: 1.938\n"
     ]
    }
   ],
   "source": [
    "mmd = utils.maximum_mean_discrepancy(summary_statistics, summary_statistics_obs)\n",
    "print(f\"Estimated MMD in summary space: {mmd:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db160622",
   "metadata": {},
   "source": [
    "## Hypothesis test for observed data\n",
    "\n",
    "In real-life modeling scenarios, a researcher might desire to perform inference on observed data $x_{\\text{obs}}$. After training the neural posterior estimator with samples from a generative model $\\mathcal{M}$, the natural question arises: \"Is the model $\\mathcal{M}$ well-specified for the observed data $x_{\\text{obs}}$?\"\n",
    "\n",
    "\n",
    "To answer this question, we can perform a frequentist hypothesis test on the summary MMD distances. First, we need to gather samples from the sampling distribution of MMD under a well-specified model. This is straight-forward because by definition the model $\\mathcal{M}$ is well-specified with respect to itself. Thus, we will generate a reference sample from the training model $\\mathcal{M}$ and estimate the summary MMD to samples from $\\mathcal{M}$ itself.\n",
    "\n",
    "**Note:** It is important that the number of simulated data sets to estimate the sampling distribution of the summary under the null hypothesis matches the number of observed data sets. This is because the MMD estimator is biased and we need comparable values for the sampling distribution and the observed summary MMD. Therefore, the `bayesflow.computational_utilities.compute_mmd_hypothesis_test` function needs either `observed_data` or `n_observed_data_sets` directly to determine the number of data sets for the estimation of the MMD sampling distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b68edbb1",
   "metadata": {},
   "source": [
    "We start by creating some observed data $x_{\\text{obs}}$ from the generative model of the trainer. We expect our model to be well-specified for these data (up to the type I error rate of $5\\%$). We simulate and test against 10 \"observed\" data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3eb59643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "66f9f28829f5492cbfaa9288f8870456",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CeXh4MCMjI2Zubs5GjBjBHj16pLJ/cHAwW7BgAWvfvj2ztrZmUqmUOTk5sZEjR7KAgIAin+PSpUts1KhRzM7Ojunp6TFra2vWunVrtmDBAnb9uur75OTkpHZvYnEaNWrEADArKyuWk5NT7H45OTls/fr1rEuXLszMzIxJpVLWoEED5unpydasWcPi4+PV9l+2bBlzcXHhz++LL75g2dnZZboPkzHG5HI5W79+PWvWrBmTSqXMzs6OffDBByw5ObnYc92xYwdr3bo1MzIyYnXq1GHvvvsuu3v3Llu6dCkDwHx9fVX2f/XqFRs3bhyzsbFhIpGIAWBLly7ltwcEBLA+ffowCwsLZmJiwrp168aOHj3KfH191fbVRRW9D5NjjLHikmmHDh1w48aNqsnctY1yZ4Ti3wLej59dxq61t/nlL9f1RK9Bzmr7PboXj8+n/YO0FEX38Tq2RjhybQzq2qnfh0YKFfSSVJGg9Ltfp0PVBkQI0boi/87LgK5hVgOBF56pJMtxs1oUmSwBoGlLa6za1x9mlvoAgIRXmfh4/Fnk5hZ9IZ8QQohmKGHqOLmc4afFAfxyY3crTPtEvZehMpemlliytidEIkUt9lbAC6z7quwdIgghhBSihKnjzh4OxcM78QAAkYjD8l89S723DADadbXDtI/b8Ms7197G3etF3x9HCCGkdJQwdVhubj7WL7vKL/cb6Yo6NkYlHKFqzMwWaNdV0XuOyRm+nnmBmmYJIaScKGHqsBP7HiEqTDEklkRPhJmL2pfpeI7jMH+5B/QNFONMhj5IxI5VQVqPkxBCagNKmDqKMYb9mwrnzHt3cjOYmuuXuRw7R1NMmd+GX9684jpiY9KLP4AQQkiRKGHqqHs3YhFyOw6A4trluNnFT+hbmhFT3NCwmSUAICcrH+t9rpZyBCGEkDdRwtRRv/96n3/crpsdzCzKXrssIJaoNuce3RXCdyQihBCiGUqYOiglKRtnfg/llyd9VPR8fmXRvnt9dOxlD0AxTsKqzwNKOYIQQogySpg66OT+R8jOygMAWNsawa11Xa2UO3NRe/7ezP/+eYZb/73QSrmEEFIbUMLUQacPFdYuR05z0+i+S004N7ZA33dc+OUNy+haJiGEaIoSpo558SwNt6+8nlyWA/qNcNVq+RM+bAWRWJGAr/pG4+Zl9amSCCGEqKOEqWP+/rNwyp+GTS1hZmmg1fLtnczw1juFM7Zv+IZqmYQQoglKmDrm3B+FzbGDxjSulOeY8EFhLfOa33M8uBVbKc9DiK6aOnUqOI5DRESE0KHwnJ2d+UmaiW6iCaR1SExkKu5cfT3eKwf0GexcKc9T38kUvQc54+IJxezwO1YF4ad9/SvluWqMO18DmVFCR1E8I0eg9TcVLsbZ2RmRkZFFbrO1tcXLly9V1slkMnzzzTfYt28fEhMT0a5dO6xatQrt2rVTO/6ff/7B22+/jRMnTmDIkCEVjrW66d27N/z9/VHCjIpEx1HC1CF/Hy1sjnWthOZYZaPea84nzHNHwrDg2y6wdzartOer9jKjAGNnoaMoXkaE1ooyNzfH/Pnz1dabmJiorVu8eDHWrFmDkSNHwsHBAXv27EGfPn3w8OFD2NnZ8fulp6fD29sbEyZM0IlkuWLFCixevBj29vZCh8K7cOGC0CGQUlDC1CF+pyL4xwMrqTm2QCP3OmjbtR6C/nsJuZxh98+38fnqnpX6nKR6sLCwgI+PT6n7McawZcsWTJs2Ddu3bwcADB8+HL1798aePXvw2Wef8fsuXrwYWVlZWLduXWWFXSZ2dnYqCV0XuLpqt4Mf0T66hqkj0lNlCAoovC+yez/HSn/OUe+5848Pb3uAlKTsSn9OUnPExcUhMzMTnTp14tcVPFZu1r106RI2bdqEDRs2oE6dOuV6roLre6mpqfj444/h7OwMPT09lcT+8OFDTJ06FQ0aNIBUKoWtrS3Gjx+PR48eqZVX0jXMq1evwsvLC/Xq1YNUKkWDBg0wa9YsxMQU3aM8MTERS5YsQYsWLWBkZARzc3O0bt0aixcvRkZGBiIiIsBxHPz9/QEoJkUo+Ondu7faOb4pJycH33//PVq2bAkjIyOYmZmhR48e+P3339X2LXiuqVOnIiIiAmPHjoW1tTUMDAzQoUMHnDx5suQX+g1//fUXJk6ciCZNmsDY2BjGxsZo3749fv75Z8jlcrX9C17Xp0+fYv369WjVqhUMDQ1VzjMxMRGff/453NzcYGhoCHNzc/Tt2xd///23WnkpKSlYuXIlPD094eDgAKlUirp162LYsGEIDAws07loA9UwdcSVi8+Ql6f4BbSuZ1SmabzKq0OP+nBuYoGIx8nIyc7H77/eh/eiDpX+vES35eTkYO/evYiKioKxsTFatWqFnj17QiwWq+xnbW0NQ0ND3Lx5k19348YNAICTkxMAICsrC++99x5GjBgBLy+vCsUlk8ng6emJxMRE9OvXD2ZmZnBxUdxXfPbsWYwYMQK5ubkYOnQoGjVqhOjoaPz55584deoUfH19i7yu+qbt27dj5syZ0NfXx7Bhw9CgQQOEhoZi27ZtOHHiBK5cuQJHx8Ivs+Hh4ejTpw8iIyPRvn17vP/++5DL5Xj8+DHWrFmD2bNnw8LCAkuXLsXOnTsRGRmJpUuX8seX1slHJpOhf//+8Pf3R7NmzfDhhx8iMzMTf/zxB8aMGYPbt2/ju+++UzsuMjISnTp1QsOGDTFp0iQkJibi0KFDeOedd/DPP/+gT58+Gr3mixcvhkgkQufOnWFvb4+UlBRcvHgR8+bNw/Xr17Fnz54ij5s3bx4uXbqEwYMHY9CgQfzvTmRkJHr37o2IiAj06NEDAwYMQEZGBk6ePIkBAwZgy5Yt8Pb25ssJCQnBkiVL0LNnTwwePBiWlpaIiorC8ePHcebMGZw4cQIDBgzQ6Fy0gRKmjrh0rvAbeRdPhyp5To7jMOo9d6xcpBgmb9e625gyvy2k+uJSjiQ12cuXLzFp0iSVdS4uLtixYwd69erFrxOJRJg5cyZ+/vlnpKSkwN7eHnv27IGZmRkmTJgAAPjqq6+QkJCAjRs3VjiuFy9eoHnz5vD394exsTG/PikpCePGjYORkRH+/fdfNG/enN92//59eHh4YMaMGbh161aJ5T9+/BizZ8+Gs7Mz/P39Va5vXrhwAf369cO8efNw9OhRfv2ECRMQGRmJ7777Dp9//rlKefHx8TAxMYGBgQF8fHzg5+eHyMhIjZq7C6xatQr+/v4YOHAgjh8/DolE8ZG9dOlSdOrUCStWrMCQIUPQtWtXleP8/Pzg4+OjkpzHjx+PAQMGYOXKlRonzFOnTqk1FcvlckybNg27d+/GnDlz0LlzZ7Xjbt26haCgIP4LTYEpU6YgMjISBw4cwNixY/n1ycnJ6N27Nz766CMMGzYMtra2AAA3NzfExMTA2tpapZzo6Gh06tQJCxYsqNKESU2yOoAxhsvnCntg9hniUsLe2tVniDPq2BoCAJLis3HygHrzFak9pk2bhgsXLuDly5fIyMjAvXv3MGvWLERERGDgwIG4c+eOyv4//PADFi1ahGvXruG3336Du7s7/vnnH9jb2+PatWtYu3Yt1q1bh7p168LHxwd2dnaQSCRo3749AgLKPp7xqlWrVJIlAOzevRvJyclYtmyZSrIEgBYtWsDb2xtBQUEIDg4usexffvkFubm5WLdunVpnoL59+2LYsGE4ceIE0tLSAAA3b95EYGAg2rRpg0WLFqmVV9AUWhHbt28Hx3FYvXo1nywBwMbGBl999RUAYNu2bWrHOTk54csvv1RZ179/fzg6OuLatWsaP39R11VFIhHmzZsHADh37lyRx3322WdqyfLOnTvw9/fHyJEjVZIloLhuvmzZMmRnZ+PIkSP8enNzc7VkCQAODg7w8vLCw4cPERVVdb3XqYapA8IeJOJltGKOSj2pCM3bamfsWE3oScUYPtkN21Yqvn1vX3ULw6dobzg+Ur0o10gARcLZvHkzTExMsGrVKvj4+KjUsPT19bFixQqsWLFC5TiZTIZp06ZhwIABmDhxItauXYtly5Zh6dKl6NatG7799lsMGDAAYWFhfG2iNAYGBmjVSn0igoJrWXfu3Cmy9vb48WMAiua9NxNqUeX4+/vj+vXrattjY2ORn5+Px48fo3379rhy5QoARSISibRf90hLS0NYWBjs7e3RrFkzte2enp4AgKAg9Unh27Rpo9aEDgANGjQo07W/hIQErFy5EqdPn8bTp0+RkZGhsv358+dFHqd8XbtAwfOmpKQU+T7FxSmmMwwJCVFZHxAQgHXr1iEwMBCxsbGQyWRqMSg3k1cmSpg6QLk5tllra4glVVvxHzy2CfZtuousjDyEP0rGVb/n8OhTNc3CpHqYPXs2Vq1ahX///Vej/b/55hs8f/6c78ixcuVK9O3bl/+gbNq0KZydnbFx40Z8841m94/a2NgU+UUuISEBALB169YSj09PL3ni9IJyVq5cqVE5ycnJAFBpt6akpKQAQLG9eQvWF8ShzMLCoshjJBJJkZ11ipKcnIyOHTsiPDwcnTp1wuTJk2FlZQWJRILk5GSsW7cOOTk5RR5br149tXUFr+/58+dx/vz5Yp9X+X06evQovLy8YGBggLfffhuurq4wNjaGSCSCn58f/P39i42hMlDC1AGBF57xjz2HNixhz8phYibF28NdcXyvojl2/6a7lDCJirp1Fa0eb9YwinL79m388MMP+OWXX2Bvb4/U1FTExMTw1zUBwNHREdbW1njw4IHGMRTX6mFubg5AUcMsqgaqqYJyUlJSYGZW+j3JBUmpuFpWRRXE8+ZgEQVevHihsp+2bdu2DeHh4Vi6dKlajTAwMLDEW4SKeq8K4ly3bh0++ugjjWL46quvIJVKcePGDbi5ualsmzVrFt/zuKrQNUyBySBGkNI0Wx171hckjmHjm/KPfU+E803EhADgmx8bNiz5C11eXh6mTZuG3r17Y8aMGSrb3qwJZGdr5zYmDw8PAIrbV6qynIL9z507p1GtraCJND8/X6PyTU1N4erqiufPnyM0NFRtu6+vLwBo1Pu3PMLCwgAAI0eOVNtWnkRVnvcpLCwMzZs3V0uWcrkcly9fLnMMFUUJU2D3OUdkZSrmvjQ1l8LWXn00larg1NgCbTwUzShyOcPvW+8LEgcRTkhISJE1yIiICMyZMwcAMHHixBLL+P777xEWFqbSPGpmZgZ7e3ucPXsWeXmK33V/f3+kpaXB3d29uKI0Nm3aNL7TSFEdWuRyOfz8/EotZ86cOdDT08OCBQv4657KZDKZyod9+/bt0bVrV75G/aaEhASVLwUF96CWpZPK9OnTwRjDwoULVRJtfHw8li9fzu9TGQpueXnztQsKClK7Zq2JDh06oEePHvjzzz/5gS7edO/ePcTGFo5t7ezsjNDQUJV7YBlj8PHxKbUTV2WgJlmBXecKR/Rxb28jYCTAsAlN+anFDm65j9lfdKRbTGqRQ4cOYdWqVejZsyecnJxgamqKJ0+e4NSpU8jOzsagQYPw6aefFnt8cHAwli9fjlWrVqndX/jZZ59h3rx56NGjBzp37ox9+/bBxMQEH374YYXjrlOnDv744w8MHz4cHh4e6Nu3L9zd3cFxHJ49e4bAwEC15FWUZs2aYfv27Zg+fTrc3d0xYMAANGnSBLm5uYiKisKlS5dQt25dPHz4kD9m79696N27N7744gscOXIEvXv3BmMMoaGh+Pvvv/Hw4UP+tejbty8OHz6MESNGYNCgQTA0NISTk5PaLTzKPv30U5w5cwbHjh1D69atMWjQIGRmZuLw4cOIjY3FZ599hu7du1f4NSzK5MmTsXLlSsyfPx++vr5o3LgxQkNDcfLkSYwYMQKHDh0qc5n79++Hp6cn3nvvPfz888/o3LkzLCwsEB0djbt37+L+/fsIDAyEjY3is3DBggWYPXs22rZti5EjR0JPTw8BAQEIDg7G0KFDceLECW2fdokoYQrsGteEf1wVo/uUpOtbDWBta4T4V5lISczG33+GYci4pqUfSGqEPn364NGjRwgKCkJAQAAyMjJgYWGB7t27Y9KkSZg0aVKx1xHz8/Mxffp0dO7cucgkOHfuXKSmpmLz5s24desWWrdujTVr1mjcQ7Y0ffv2xd27d/HTTz/h3LlzuHTpEqRSKerXrw9PT88imxWLMnHiRLRu3RqrVq2Cr68v/v77bxgbG6N+/frw8vLCmDFjVPZ3cXHBrVu38OOPP+Kvv/7Chg0bYGBgAGdnZ3zyySf8Bz8AzJgxA5GRkTh48CB+/PFH5OXloVevXiUmTKlUivPnz2P16tXYv38/1q9fD4lEgtatW2Pt2rUYN25c+V4wDdSvXx+XLl3C4sWLcfnyZZw7dw7NmjXDpk2b8NZbb5UrYTo4OODmzZtYv349jhw5gn379iE/Px/16tVD8+bNMXfuXLRs2ZLff9asWdDX18fatWuxa9cuGBoaokePHtixYweOHDlS5QmTYyUMnd+hQwd+5A6iZRwHGSToIvkB2ZwUALDv35GwsTMu5cDKtXfDXexadxsA0KqTLQ5cHiVoPFUlJCRE7ToJEpR+96OP14rZSmqLsWPH4tChQ4iJidG5MWVJ5Sny77wMqIYpoHucE58szSz0BU+WgGIOzn2b7iIvV467114h5HYc3NpU3X2hOouSUY3y+PFj6Ovr871/CdEEdfoR0DWuEf+4RQdhr18WsKpriB79C5uGD2y+J2A0hGjXunXrMGLECAQFBeGdd95RGT2HkNJQwhTQTaWE2a1fAwEjUTVsYuGoIif2PUJqctXdGExIZVq7di2uXr2K6dOnY8uWLUKHQ6oZ+nolkDyIcIdz5pdbd1IfGUMo7u3qomEzSzx9mARZTj5O7HuICR+2FjosQiosPDxc6BBINUY1TIGEwg6ZnGJgZiMTPdjUF/76ZQGO4zBkXGHv3QOb76GEvmGEEFIrUMIUyG1R4YgpjZpb6dxg555DXWBgpGiACH+UjFtKoxERQkhtRAlTIEFc4dQ3nXpXzuDNFWFsKkXfoYUxHtpCI/8QQmo3SpgCuc0V1jDbdNad65fKlJtlzx0JQ1J8loDREEKIsChhCiA2Jh3POcW4kgZMBlc3K4EjKloj9zpo2koRZ16uHMf2PCzlCEIIqbkoYQogKLBwup6WLBISPd19G4aMpc4/hBACUMIUxO0rhR1o2jDd7ubea7AzjE31AADR4am46lc5c/8RQoiuo4QpgLvXXvGP27CnAkZSOkMjPbz1buH11kO/0sg/hJDaiRJmFcvPl+PRnXh+uQXT4QG9Xxus1Cx74a+niHupPmciIYTUdJQwq1j4wyR+wmgblgxrpAkcUelcmljyY93m5zP8tStE4IgIIaTqUcKsYsFBcfzj5uyZgJGUjXIt8+Cv95GfLxcwGlLZ+vXrB47j4OfnV+w+s2fPBsdx+PXXX6suMEIERAmzij1QSpju1Shh9hzgBFMLxVRkL5+l47/z1Sd2UnY3b96EWCxGx44di93n6tWrABTz5hJSG1DCrGIhSgnTrRolTKm+GP1HFs6ucmgrjfxTUz19+hSJiYlwd3eHsXHRYxxnZmbi/v370NfXR8uWLas4QkKEQQmzCsnlDCG3lWuYut/hR9ngMYXNsn6nwvEyOl3AaEhluXHjBgCgc+fOxe5z8+ZN5OXloVWrVtDT06uq0AgRFCXMKhTxOBmZ6bkAgDosFXWRKnBEZePgYoY2XRTD+DE5cGT7A4EjIpVBk4R55coVANQcS2oXmg+zCgUHxfKP3dkz6Nb8JJoZMq4Jbr8eqejwtgeY9UVHSCT0vasmKUiYhw4dgq+vb5H70PVLUhtRwqxCwdX0+qWyrn0bwKKOAZITshH3MhP/no6A57CGpR9IqgXGGG7dugUAOH/+fKn7U8IktQklzCoUXE17yCrTk4oxYFQjHNys6PRz6Nf7NT5huttdAXBF6DA08kA2p0LHh4aGIiUlBR4eHggMDCxyn6SkJFhZWcHQ0BDu7u4q2zZt2oSVK1fixYsXcHd3x9q1a9GjR48KxUSIrqC2tCoilzOVHrLNq1mHH2WDRjdGwXzXAeejEB1eva7FkuIVNMe2a9eu2H0KaqBt2rSBWCzm1x86dAjz5s3DF198gaCgIHTt2hUDBw5EVFT1/V0nRBklzCry7EkK0lNlAABDYwnqIVnYgCrAroEpOvSoDwBgDPjjN+r8U1OUJWG+2Ry7evVqTJ06Fd7e3nBzc8P69ethZ2eHX375pfICJqQKUZNsFVFujnVtZgWuerTwFWvIuCa4/m8MAODIjmB88HUnSKXiUo6qnh688ADq1I5rdZokzKCgIACqCVMmk+HmzZv49NNPVfbt168f/vvvv0qIlJCqRzXMKvLgllIP2dfjslZnnXs7wNrWCACQGJcF3+O6PU0ZKZ1cLkdQUBCkUqnatUllRdUw4+PjkZ+fD1tbW5V9bW1t8fLlSxBSE1DCrCLKAxY0bVFHwEi0QywRYeBopZF/aNqvau/hw4dIT0+Hu7s7pFJpkfukp6cjNDQUJiYmaNasWRVHSIiwKGFWAcaYSpNs4xqQMAFg4KjGEIkUvX+u+j1HxOMkgSMiFaFJc+ydO3cgl8vRtm1biESFHx/W1tYQi8V49eqVyv6vXr1CvXr1KidgQqoYJcwqEB2eitSkHACAgaEEtvZFj89Z3dS1M4aHpwO/fJg6/1RrkydPBmMM27ZtK3afbt26gTGGf//9V2W9VCpF+/bt1e7dPH/+PLp27Vop8RJS1ShhVgHl2mVDN0twXHUc46doytN+/bkjBDnZeQJGQ4T08ccfY+fOndi2bRtCQkIwb948xMTEYPbs2UKHRohWUC/ZKhCs3OGnXfXv8KOsfXc71HMwwcvodKQm5+D8n08wZHxTocMiAhgzZgwSEhLwv//9Dy9evECLFi1w+vRpODk5CR0aIVpBNcwqoFzDbNqyZly/LCAWizBwdGN++dCvNO1XbfbBBx8gIiICOTk5uHnzJnr27Cl0SIRoDSXMSqbW4ce9ZiVMABjg1QhiiaKZ+dZ/LxD2IEHgiAghRPsoYVayF1FpSE7IBgDoG4hh52gicETaZ1XXEN3eduSXf99GnX8IITUPJcxKply7dGlaszr8KFPu/PPX7hBkZuQKGA0hhGgfJcxK9uCW0gwl7eoKGEnlauNRD/bOpgCAjLRcnD0cKnBEhBCiXZQwK5nypNFNWloLGEnlEok4lVomdf4hhNQ0lDArEWMMwUo1zCY1ZISf4vQb7go9PcWv1P0bsSpfFgghpLqjhFmJXj3PQGJcFgBAqi9GfSdTgSOqXOZWBug5sPCeu9+3Vq/OP4wxoUMghFQSbfx9U8KsRModfpwamfPjrtZkQ8YVNsue2P8IGWkyAaPRnFgsRm4udVQipKbKzc1VmfC8PChhViKVEX7a16wRforj3t4GTo3MAQDZmXk4eeCxwBFpxtTUFKmpqUKHQQipJKmpqTA1rVgrHyXMSqR8Da9pq5rb4UcZx3EqtcwDm+9Vi6ZOKysrJCUlIT4+HjKZrFrETAgpGWMMMpkM8fHxSEpKgpWVVYXKo7FkK5HqCD8Ve6Oqk7fedcW2lbeQk52P0PsJuHvtFVp31u0pnvT19eHo6IjExEREREQgPz8fyIgv3CE2RLjgCCHlJhaLYWpqCkdHR+jr61eoLEqYlSTuRQbiXmQCAPT0RHBwMRM4oqpjYiZFnyEuOPtHGABg38Y7Op8wAUXStLOzg52dnWLF/uaFG8dTjZOQ2o6aZCuJcu3SoaE5xOLa9VK/M7FwxpKzh8MQG5MuYDSEEFJxtetTvAo9UOnwU3NH+ClOI/c6aNlB0dEpP5/h4BYayIAQUr1RwqwkygMWuLWufQkTAN6d7MY/PrjlHk0uTQip1ihhVpKQ20odflrUng4/yrq93QA29Y0BACmJOTj9O40vSwipvihhVoKE2Ey8jFZcsxNLODg2NBc4ImGIJSIMm1B4LXP3utt0uwYhpNqihFkJlDv82DuZQSypvS/zwNGNoW+gGF3j8b0E3LwcI3BEhBBSPrX3k7wSKV+/bN62dl6/LGBmoY+33mnIL+/dcFfAaAghpPwoYVYClSHxavAcmJp6d0ph559/jj1BTCQNQUcIqX4oYVaCYJUOPzV7Si9NODe2QLuuisEAmBzYu5FqmYSQ6ocSppYlJ2QhJjINACASc3BqZCFsQDpixNTCWubvW+8jLSVHwGgIIaTsKGFq2QOl65d2DiaQ6NFLDAAde9nD0VXRWzgrIw9HtgcLHBEhhJQNfZprmXIP2Wata8cMJZoQiTh4vVc4NuvOtUHIzc0XMCJCCCkbSphaptzhp2VHWwEj0T19hzWERR0DAEDci0ycOxwmcESEEKI5SphapjzCT5OW1OFHmVRfjHcnNeOXf1t1iwYyIIRUG5QwtSglKRvPnipumRCJqMNPUYaOb6IykMFV32iBIyKEEM1QwtSiEKXrlzb1jSHVFwsYjW4yszRAf69G/PKO1UECRkMIIZqjhKlFyh1+qDm2eCOmuoHjFI8v/x2F0PsJwgZECCEaoISpRcpD4rXuXE/ASHSbvZMZur3tyC9vX31LwGgIIUQzlDC1KDiosIdsU6phlmjUDHf+8akDjxETlSZgNIQQUjpKmFqSlpKDyLAUAADHAS5NLQWOSLc1b1sXrTopbrvJz2fYuYauZRJCdBslTC1Rvp3Eup4RdfjRwNjZLfjHh7c9QGJcloDREEJIyShhaolyh5/G7tQcq4kO3eujUXMrAIAsJx97N9wROCJCCCkeJUwtUe7w08aDOvxoguM4lVrmvo13kZ4qEzAiQggpHiVMLVG+B7NpKxpDVlPd+znC3tkUAJCeKsPvW+8LHBEhhBSNEqYWZKTJEP44SbHAAQ2bUYcfTYnFIoz2Lqxl7lgdhJzsPAEjIoSQolHC1IKHd+JRMCRqHRtDGBhKhA2omnnrnYaoY2sIAEiMy8KxPQ8FjogQQtRRwtQC5Q4/jdysBIykepLqi+E1vfC+zF+/v0FTfxFCdA4lTC1QntKLOvyUz+AxjWFuqQ8AePEsHSf2PRI4IkIIUUUJUwtUJo1uU1fASKovQ2M9lQmmf/n2OvLy5AJGRAghqihhVlBGugxPHybxy67U4afchk1oBlMLKQAgJjINpw48FjgiQggpRAmzgkKC4iCXK3r8WNU1hKGxnsARVV9GJnoYOVWplvnddeTnUy2TEKIbKGFW0P0bhdcvaUqvint3cjMYmyq+dDx7koIzv4cKHBEhhChQwqwg5YTZrqudgJHUDMamUoyY6sYv//I/qmUSQnQDJcwKunfjFf+4eTvq8KMNw6e4weh103ZEaDLO/REmcESEEEIJs0KS4rMQHZ4KABCJODSkKb20wtRcH+9ObsYvb1p+jWqZhBDBUcKsgPs3C5tjbeobQU9KU3ppy8hphbXM8MfJOH2QrmUSQoRFCbMC7l0vbI6lAde1y8zSACOnF17L3PDNVRr9hxAiKEqYFaDc4adD9/oCRlIzjZzWHKbmivsyo8NTaYxZQoigKGGWE2NMpUnWrS11+NE2Y1MpRnsXjjG7afk1yHKolkkIEQYlzHJ68SwdCa8yAQASiQgNGpoLHFHN9M6kZrCoYwAAePU8A4d/eyBwRISQ2ooSZjndV7qdpL6TKUQiTsBoai5DIz2MnVU4X+bm764jKzNXwIgIIbUVJcxyUr5+2ZyaYyvVkHFNCufLjM3CgV/uCRwRIaQ2ooRZTsrXLzv0oA4/lUnfQIIJ77fil3/9/gZSkrIFjIgQUhtRwiwHuZzhgXKHnzZ0S0llGzCqEewamAAA0lJk2PbjLYEjIoTUNpQwyyHicTLSU2UAAH1DCeraGQscUc2nJxVj+idt+eU9628jJipNwIgIIbUNJcxyUO7w49TIHBxHHX6qQs+BzvyMMLkyOTYsuypwRISQ2oQSZjkoD7jeunM9ASOpXUQiDt6fteeXj+99iEd34wWMiBBSm1DCLAflHrJtPChhVqU2HvXQubc9AIAxYPUX/wkcESGktqCEWUYyWT4e3ims1TSlSaOr3HsL2/H3vV7+OwpXLj4TOCJCSG1ACbOMHt6O44dnM7WQwtzKQOCIah+XJpboN8KVX/7xswCa/osQUukoYZbRrf9e8I+b0Qwlgpn8UWvoGyimU3t0Nx5HdgQLHBEhpKajhFlGtwNf8o879bIXMJLara6dMcbMLBwyb+2SQBrMgBBSqShhlgFjDEGBhTVM6iErrNHe7rC1V9wDm5KUg43fXBM4IkJITUYJswyeR6Qh/qVihhI9PREcG9EMJULSN5Bg1uIO/PKBzffw+B7dZkIIqRyUMMsgSOn6pWMjc4jF9PIJrXt/R7Tpoqjpy/MZVnxyCYwxgaMihNRE9IlfBkFXChNm++404Lou4DgOH3zZESKx4jaTa37Pcf7oE4GjIoTURJQwy0C5htm+m52AkRBlLk0sMWxCU375+08uISNNJmBEhJCaiBKmhtJSchB6P4Ffbtaa5sDUJZM/ag1zS30AwKvnGfh56RWBIyKE1DSUMDV0K+AFCi6N1bUzgpGJnrABERWm5vqYvaQjv7xv413cufqyhCMIIaRsKGFq6Pq/z/nHbbtQc6wu6jvMhZ/MmzHg69kXIZPlCxwVIaSmoISpoRuXYvjHXd9qIGAkpDgcx2HeNx7QN1SMABT2IBHbf6KJpgkh2kEJUwMZaTIE3yqcoaRlR1sBoyElqedggmkLCiea3vztdTx9mCRgRISQmoISpgZu/fcC+fmKC5jW9YxgZqEvcESkJO9OblY40XSuHEvfv0iDsxNCKowSpgZuKF2/bN2Zape6TiwW4eNvu/D3Zt4KeIHtq4IEjooQUt1RwtSAcoefbm85ChgJ0ZSrmxXGv9+SX17vcwUPlJrVCSGkrChhliIjXYYHN+P45VadqIZZXUz4oBWatVZMwZafx/DZ5L+RlZkrcFSEkOqKEmYpbl6KQV6e4vpXHRtDmjC6GpHoibB4VXcYGEkAABGPk7FyUYDAURFCqitKmKUI+OcZ/7jgHj9Sfdg7meGDLwsHNDi05T78ToULGBEhpLqihFmKQKWE2WuQs3CBkHIb4NUI3d4uvPa8ZMYFvHiWJmBEhJDqiBJmCV5Gp+NJSCIAQCTi6P7LaorjOHz8rQesbAwBAMkJ2Vgw9gxkOTQKECFEc5QwSxB4IYp/7NjIHAaGEgGjIRVhZmmAJWt68rea3LseixUf/ytwVISQ6oQSZgn+U2qO7dGPbiep7lp1ssWsRe355d+3PsCfO4MFjIgQUp1QwiyGXM4QeKEwYdL4sTXD8Klu6D3YmV9ePtcf92++Ei4gQki1QQmzGMFBsUiKzwYA6BuK0dDNSuCIiDZwHIePv+sC5yYWAABZTj7mjTqDuJcZwgZGCNF5lDCL4Xsign/csoMtRCJOuGCIVhka6WHpxt78nKYvo9Px4bsnkZEuEzgyQoguo4RZDN+ThffqDfBqJGAkpDI4OJthydrCTkAPbsVh4cS/+UEqCCHkTZQwixATlYZHd+MBAJwI6NjTXuCISGXo1Msec5d25pf9T0dgxceXwBgTMCpCiK6ihFkE5ZFgGja15JvuSM0zZFwTjJ3Vgl8+uPkedq6hmU0IIeroxsIiKDfHvvWuq4CRkKow7eO2eBmdDr9TEQCAnxb/B4s6BhhO35MIIUqohvmG9FQZrvkVTufVc6CTgNGQqiAScVj4Qze07GDDr/t6li/OXG0nYFSEEF1DCfMNfifDkZer6PhhXc8INnbGAkdEqoJUX4xvtnjC1c0SgOI+3MW/TsbFoBalHEkIqS0oYb7h9O+h/OO+7zQUMBJS1UzMpPh+x9twamQOAMjLF+PjjdMRcL+ZwJERQnQBJUwlyYnZCDhfOH7s4NGNBYyGCMGijgF+2PU26juaAgBy8/Tw0c/e+O+fqFKOJITUdJQwlVw49pRvjrW1N4bd6w9NUrvUsTHCj7vfRj0rxUw12TIpPhx+kubRJKSWo4Sp5OzhwuZYGqygdrO1N8H2Rev5pCnLkeOjUadx7kiYwJERQoRCCfO1+FeZuHIxml/uP5ISZm3nZBuP3Z+vQ4O6cQCA/DyGTyecw4l9jwSOjBAiBEqYrx3f+xByuWKEFwdnM9Sl3rEEgH3dROz6Yh0cG+QCUPSe/Xz6eez++bawgRFCqhwlTACMMRzZXjgv4vCpbgJGQ3SNrWUKflkdD1cXxeDsjAE/fHoZP352mf+SRQip+ShhArh5OQYRockAAImeCG8Pp9tJiKo6VnJsWh2Plu45/Lpda29j4cRzyMnOEzAyQkhVoYQJ4I/fCmuXnXvbw9CIxkQj6szNGNavjEev7ln8urN/hGHm4ONITsgq4UhCSE1Q6xNmSlI2/v6zsOfjuPdbChgN0XUG+sB3XyfC6910ft2NSzEY0/UwHt+LFzAyQkhlq/UJ8/etD5CTnQ9AMRRe05bWAkdEdJ1YDHwyJwVzZqbw66LDUzG+5x8qX74IITVLrU6Yspx87N1wh19WnuaJkJJwHDBxTDp+WJYAQ0PFYBdZGXlYMPYsfl56Bfn5NBE1ITVNrU6YJw88QvzLTACAgZEEA0fRUHikbHp1z8ZvG+LgUD+XX7dlxQ3MGHgMr56nl3AkIaS6qbUJUy5n2LG6cKLgdyc1g1RfLGBEpLpq6JyH7Zvi4NExm193ze85RnQ4iIsnaDg9QmqKWpswLxx7iqcPkwAAYgmHMTOpOZaUn5kpw6pvEzB9YipEnOLezOSEbMwdeQrLP/JHVmZuKSUQQnRdrUyYeXlyrP0ykF/uO6whTMykAkZEagKxGJg5LQ0bV8fDxrrw3syDm+/h3bYHVGbCIYRUP7UyYf65M5gfqEAs4TBrcXthAyI1SttWMuzZGovePQrvzYwOT8XMwcexeOp5JMbRPZuEVEe1LmFmZuRi0/Jr/PKIqW4wszQQMCJSE5mbMaxYmogvFybB1CSfX39i/yMMabkX+zbdhUyWX0IJhBBdU+sS5qbl1xD34nXPWEMJJn/URtiASI3FccCQAZk4tDMW/Twz+fUpiTn4bv6/GNZ6P84eDgVjNB4tIdVBrUqY92++wq61t/nl6Z+0hYGhRLiASK1gZSnHN0uSsGZFPOxsCzv/PHuSgk8mnMPYbofhdyqcBnInRMfVmoSZm5uPr2f58h9Kjo3M8e7kZgJHRWqTLp1ycHBnLObOSlFppr1/IxYfDj+Fd9sewNFdwZDlUFMtIbqo1iTM1V/8h0d3FWN9ikQclm/uA47jBI6K1Db6UmDC6HQc2fsKE0anQU+vcESgJyGJ+NL7Ivo33Y2N31xFTGSqgJESQt5UKxLm2cOh2L1OdQi8+k5mAkZEajszU4a5s1JxZM8rjB+VBkODwlplbEwGNv3vOvo12Q3vwcdw9nAoTSFGiA7gWAk9Djp06IAbN25UZTxaFxIUh0mefyIrQ3HtqGmrOlj/xyDBa5f1nc35xzERKSXsSYRSP6DwPUprsLhSnystncPRE8Y4dMQICUnq08sZmeih92Bn9B/ZCN37O9G1d0IEUKMTZlhwIqa+9SeS4hVDlpmYSbHXbwSMTYUfpIASpu6ryoRZQCYDLgUa4NgpI1y/ZQDG1L/YGRrroWvfBug+wBE9+jvBroFplcRGSG1XY7+mPr4Xj5lDjvPJUizh8NOefjqRLAkpjlQK9O2Vjb69svHilRinzhnhzN/6eP5Cn98nKyMXF44/xYXjTwEArm5W8PB0QLuudmjXzQ429U2ECp+QGq1G1jD9ToVj4aS/kZmuaIYViTms3NMPrTraChxZIaph6j4haphFYQwIeyrBxX8N8c9FfTyL0S9xfwcXM7Traoe23ezQsoMtXN2saGIBQrSgRtUws7PysN7nCnatvY2CrwEiMYdvt/bVqWRJSFlwHNDYNQ+NXdMwc2oaoqIlCLymj4BAKW7fM0BunmrfvejwVESHp+L4vkcAALGYg0tTSzRtZY0mLeugaUtrNGxmCVsHE0gktaLfHyFaUSMSJmMMF449xZolgfwYsYCio8Tqff3h2txKuOAI0SKOA5wa5MGpQR7GjsxAdjaHO/eluHNPiptBYoQ8NoAsV7U2mZ/PEBaciLDgRJw6WLheIhHBztEE9s5mcHAxg4OzGeo5mKKOrSHq2Bihjq0RLK0NIBZTUiUEqOYJMzsrD+f+CMO+jXfw4FacyjbnJhb4cffbsKxjKFB0hFQ+AwOGzh1y0LlDDjANyM0FHoXp4c49fdy5J0FomBgvYoseKzkvT45nT1Px7Gnx93tyHGBpbQhLa0OYW+nD3NIAZpb6MLfSh5mFwev/9WFuZaD4sVBsM7XQp0RLapxqlzBfPU/Hzcsx8DsVgX/PRCAtRaayXSzhMP2Tthj1nrvgt44QUtX09IAWbrlo4ZaLCaMV6zIyOTwN10PYUwkeh0nwOFSEmBd6SEotvQMcY0BiXFa5ZlgxNZcqkqulIpmaWeirJNjCbUrJ11IfRsZ69LdLdJJOJUy5nCEzPRdpKTmIe5mJV9HpePU8HS+j0/H0YSIe30/Ei6i0Io/lRIDnEBfM/qIjLOrQ7COEFDA2YmjpLkNLd9Uvl9nZHF68FCPmpRgxLyWIfi5GbCxDQgKHxGQxklP0kJ6pfk+optJSZEhLkeF5RNF/s8WR6Ilgbqlam+Ufv/m/lT6MTaTQN5TA0EgCfUMJDIwk0NMT1aikK5cz5MryIctR/cnNyYdMaX2uTI5cmeL/vNzXj1//zy/L5MjLKxxhquB1Kni5OI4DxwF6UhGk+mLoScXQ0xdDKhXz6wrWS5XW6xXsKxWp/l+D3osqSZiynHy8/86J12+i6pua+3o5I02GjLSyz0pvai7F4LFNMGKqGyytqfmVEE0ZGDC4OOfBxTkPQE6R++TlAUnJIiSniJCaJkJaugipqYr/U1I4pKYBKckcUlI5pKZzSEsXIz1DjIwKJNq8XDkSYrOQEFv+eUNFIg4GRhIYGEog1RdDLOEgloggFr3+XyKCRMJBJBZBLOEgkYjAiTi8+bn+5gd9WbfL5Qz5+QzyfIb8fDmYyjJTWpZDLmf8/oXJsTDZVWdqSbTgsZ7ivRGJFD8cB3BFPuYgEuH1e/R6nQj84wYNzbBkba9KP48qSZgiMYcrF6O1UhYnAqxtjdCivQ06ezrAwdkMHMch7mUm4l5mll6Ajqiv9Pjx/QTB4iDFU36Pkl/FChaH0PQA1DVR/GgqXw5kZoqRli5GWoaE/z89Q/w6qUqQmsrxSTg9Q4L0TAkysvQgy634x1JBa1XBrWVEWIpKUuUl/SYt61Ra2cpKvA/T2toazs7OWnuy1OQcPHuq+T2H+SwdEpEp9KQiiEQ1o0r/ppy8NOhLavZILdX5HEUcg5lhKjiu9Km30rJyYGpY8j2S1Z3w58gAJn/9f8nvScHWokZLYvw/6tvSc2Qw0a+qAU4YwBUVxet1r3/vyvfpV1Awp7aseB+Lu3TFVB8z5XWlv+4llsg4FHd0YbFlP1szuwbQf+N84uLiULdu3TKXBShy39mzZ9XWl5gwhVZdB04oCzrHmqM2nCedY81A51g+1O+bEEII0QAlTEIIIUQDOp0wZ86cKXQIlY7OseaoDedJ51gz0DmWj05fwySEEEJ0hU7XMAkhhBBdUeUJ89mzZ/Dy8oK5uTnMzMwwYsQIREVFlXqcj4/P6xEo1H8MDHRrZJ/yniMAREVFYcqUKXB0dIShoSGaNGmCL7/8EhkZGZUcddlV5DzDw8Ph5eUFCwsLGBsbo0+fPjrZay86Ohpz585Fly5dYGRkBI7jEBERodGxcrkcK1asgLOzMwwMDNC6dWscOXKkcgMuh4qc4+rVqzF06FDY2dmB4zj4+PhUaqzlVd5zfPz4MebNm4dWrVrBxMQEdnZ2GDZsGO7cuVP5QZdRec8xLS0No0ePRqNGjWBsbAwLCwt06tQJe/furfygy6giv6vKDh48CI7j4ODgUKbjqjRhZmZmwtPTEw8fPsSuXbuwZ88ehIaGok+fPqUmhBkzZiAwMFDl559//oFEIsGwYcOq6AxKV5FzzMjIwFtvvYV///0Xy5cvx+nTpzFjxgysWrUK06dPr6Iz0ExFzjMhIQHdu3fH/fv3sWXLFhw8qJhCo0+fPggJCamK8DUWFhaG33//HZaWlujRo0eZjv3qq6/g4+ODOXPm4MyZM/Dw8MCoUaNw+vTpSoq2fCpyjlu3bkVsbCzefffdyglOS8p7jn///Td8fX0xZcoUnDhxAps2bUJcXBw8PDxw8+bNSoy47Mp7jjKZDBKJBJ9//jmOHz+O/fv3w83NDZMmTcKaNWsqMeKyq8jvaoHk5GTMnz8f9erVK/vBrAqtXbuWiUQiFhoayq97+vQpE4vFbNWqVWUub/fu3QwAO3nypDbDrJCKnOO5c+cYAHbu3DmV9YsWLWJisZhlZGRUSszlUZHzXL58OROLxSwsLIxfl56ezmxsbNioUaMqLebyyM/P5x9v3bqVAWDh4eGlHvfq1SsmlUrZ119/rbLe09OTtWzZUtthVkh5z1H52NzcXAaALV26tBIirLjynmNcXByTy+Uq65KTk5mFhQWbNGmStsOskIq8j0Xx8PBgLVq00EJk2qONc/T29mb9+vVjU6ZMYfb29mU6tkprmMePH4eHhwcaNWrEr3NxcUG3bt1w7NixMpe3a9cu2Nraon///toMs0Iqco4ymWJwbDMzM5X1FhYWkMvlYDrUP6si53nlyhU0btwYrq6u/DpjY2P06NEDJ0+eRF5eXqXFXVYiUfn+RM6dOweZTIaJEyeqrJ84cSLu3buH8PBwbYSnFeU9x4oeW5XKG6e1tbXaeLHm5uZo0qQJnj9/ro3QtEbb70WdOnUgkejU/BwVPseAgADs3bsXGzduLN/zV+jZy+jBgwdo0aKF2np3d3cEBweXqaxnz57B19cXEyZM0Kk3tSLn+NZbb6Fx48ZYtGgRgoODkZ6ejosXL2LdunWYPXs2jI2NKyvsMqvIeYrFYkil6kOP6evrIysrC0+ePNFanEJ58OAB9PX1Vb5QAIrXB0CZf9+J7khMTMT9+/fh5uYmdChaxRhDXl4eEhIS8Ouvv+LcuXNYsGCB0GFpTW5uLmbOnImFCxeq/V1qqkoTZmJiIiwtLdXWW1lZISkpqUxl7d27F3K5HFOmTNFWeFpRkXM0MDDA5cuXIZfL4e7uDlNTU/Tt2xdDhgzBhg0bKivkcqnIeTZt2hShoaFISCgcdF4ul+PatWt82dVdYmIiLCws1GonVlZW/HZSPc2dOxeMMcyfP1/oULRq48aN0NPTg7W1NebMmYN169Zh8uTJQoelNT/88ANycnLw+eefl7uM6tGeUoTdu3ejbdu2aNWqldChaE12djbGjBmD2NhY7NmzB/7+/li5ciUOHTqEDz/8UOjwtGb27NmQy+WYPHkynjx5ghcvXuCjjz7imymrSzMfqX1WrFiB/fv3Y8OGDeWupeiqMWPG4Pr16zhz5gxmzJiBuXPnYsuWLUKHpRVhYWH49ttvsWHDhgrdVVGlbZmWlpZF1j6Kq60U59q1a3j48CHWrl2rxei0oyLn+Ntvv8HPzw9hYWH89b2ePXvC3NwcM2fOxOzZs9G6detKibusKnKeDRs2xL59+/Dhhx/yHzrt2rXDggUL8NNPP8HOzq5SYq5KlpaWSE5OBmNMpZZZULMsqGmS6mPz5s344osv8L///U/neq1rQ926dfnZPQYMGIDMzEx8+umnmD59OvT0yj+/qS746KOP4OnpCQ8PDyQnJwNQ9BlhjCE5ORn6+vowNCx9PuUq/Srv7u6OBw8eqK0PDg5G8+bNNS5n165d0NPTw/jx47UZnlZU5Bzv3bsHS0tLlc4wANCpUycA0KlbLir6Xo4cORLPnz9HcHAwwsLCcPPmTaSnp6NBgwZwdHSsjJCrlLu7O3JyctSuxxZcuyzL7zsR3p49e/DBBx/gk08+wZIlS4QOp0p06NAB6enpePXqldChVFhwcDBOnz4NS0tL/ufAgQOIiYmBpaWlxs20VZowhw0bhitXruDp06f8uoiICAQEBGh8L6VMJsPBgwcxcODAcs91Vpkqco716tVDUlISwsLCVNZfvXoVAGBvb6/9gMtJG++lWCyGm5sbXF1dERMTg0OHDuH999+vrJCr1IABA6Cnp4d9+/aprN+7dy9atGgBFxcXgSIjZXX06FFMmzYNM2bMwE8//SR0OFXG398fJiYmsLGxETqUCjt48CB8fX1Vfvr37w9ra2v4+vpizpw5mhVUpptQKig9PZ25urqyFi1asL/++osdO3aMtWrVirm4uLC0tDR+v4iICCYWi9myZcvUyjhy5AgDwI4cOVKVoWusIucYHh7OTE1NWePGjdnOnTvZxYsX2Y8//shMTU1Z+/btVe5BElpFzlMmk7H58+ezo0ePsgsXLrCff/6Z2dnZse7du7OcnBwhTqdEhw8fZocPH2azZ89mANimTZvY4cOHmZ+fH7+PWCxm06dPVzlu0aJFTF9fn61atYr5+vqy2bNnM47j2IkTJ6r6FEpV3nO8fv06O3z4MDt06BADwEaNGsWXpUv3DTNWvnP09/dn+vr6rF27diwgIIAFBgbyP7du3RLiNEpUnnPcvHkzmzp1Ktu7dy/z8/NjR44cYWPGjGEA2Pfffy/EaZSovL+rbyrPfZhVmjAZYywyMpKNGDGCmZqaMhMTE/bOO++o3XgaHh5e7E3Qw4YNY1ZWVjr5wVqgIuf44MEDNmrUKObg4MAMDAxY48aN2SeffMISExOr7gQ0VN7zzM3NZYMHD2Y2NjZMKpWyhg0bsiVLlujcB2wBKCaMV/vp1auXyj5TpkxROS4vL48tX76cOTo6MqlUylq2bMkOHz5ctcFrqLznOGXKlGKPrchN85WhPOe4dOnSYo9zcnKq8nMoTXnOMSAggA0cOJDVq1ePSaVSVr9+fda3b1+dGhBGWXl/V99UnoRJs5UQQgghGqD++4QQQogGKGESQgghGqCESQghhGiAEiYhhBCiAUqYhBBCiAYoYRJCCCEaoIRJCCGEaIASJiHlxHEcOI6DSCQqcQ7PPn368Pvu3LlTZdvUqVP5bV9//XWxZezatYvfr3fv3irb/Pz8+G0FP0ZGRrCzs0PPnj2xcOFCBAUFVeRUCSGghElIhUgkEjDG8NtvvxW5PTQ0FH5+fqVOci6RSLBjxw7k5+cXuX3r1q2lluHk5ISlS5di6dKlmD9/PoYMGYKMjAz89NNPaNeuHSZMmID09HTNTowQooYSJiEVYGtriw4dOmDHjh3Iy8tT275t2zYAwNChQ0ssZ8iQIYiOjsbZs2fVtoWEhCAgIKDUMpydneHj4wMfHx9899132Lp1K27evImgoCC0bNkS+/fvx6hRo8pwdoQQZZQwCakgb29vvHz5EidPnlRZn5ubi507d6Jr166lTuc1YcIEGBoaYuvWrWrbCtbNmDGjXPG1adMG//zzD+rWrYuzZ8/ir7/+Klc5hNR2lDAJqaBx48bB2NiYr00WOH78OGJjY+Ht7V1qGRYWFhg1ahROnTqFly9f8utzcnKwe/du9OrVC02aNCl3jDY2Npg1axYAqE05RgjRDCVMQirI1NQUY8eOxdmzZxEdHc2v37p1K8zMzDB69GiNyvH29kZeXh527NjBrzt69CgSEhI0SrqlKegsdO3atQqXRUhtRAmTEC3w9vZGfn4+tm/fDgCIjIzE+fPnMWHCBBgZGWlURvfu3dGsWTNs27YNBZMIbd26FZaWlhg5cmSFYyyYgDwuLq7CZRFSG1HCJEQLOnfujJYtW2L79u2Qy+XYtm0b5HJ5mWuG3t7eePr0KS5evIiwsDD4+vpi0qRJMDAwqHCMBUmY47gKl0VIbUQJkxAt8fb2RmRkJM6cOYMdO3agffv2aNu2bZnKmDx5MvT19bFt2za+pqmN5lgAiImJAQDUrVtXK+URUttQwiRESyZNmgRDQ0PMnj0bz58/x8yZM8tchrW1NYYPH46jR49i+/bt6NKlC1q0aKGV+Hx9fQEoasOEkLKjhEmIllhYWMDLywvR0dEwNjbGuHHjylWOt7c3cnJyEBcXp7XaZWxsLLZs2QJAcQsLIaTsSh46hBBSJv/73/8wYsQI1K1bF6ampuUqo0+fPjh27Bjkcjn69+9f4Zju3LmDyZMnIz4+HoMGDcKwYcMqXCYhtRElTEK0yNHREY6OjhUqg+O4ciW1iIgI+Pj4AFAMmhAfH4+bN2/i5s2bAICJEydi8+bNFYqNkNqMEiYhNURkZCSWLVsGADAwMICFhQUaN26MTz/9FBMmTECbNm2EDZCQao5jBX3NCSGEEFIs6vRDCCGEaIASJiGEEKIBSpiEEEKIBihhEkIIIRqghEkIIYRogBImIYQQogFKmIQQQogGKGESQgghGqCESQghhGiAEiYhhBCigf8DN8qwtdAcLiMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
    "observed_data = trainer.configurator(trainer.generative_model(10))\n",
    "\n",
    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
    "    observed_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=False\n",
    ")\n",
    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df48417a-26a8-4ba2-8bbf-b1264cd99681",
   "metadata": {},
   "source": [
    "Now, let's plug in some observed data from a different generative model. We will use the generative model from above, where the prior covariance is larger. We see that the misspecification is clearly detectable. In practice, we would conclude that our neural posterior estimator $q_{\\phi}$ is not trustworthy and we should reiterate on the generative model we use to train the neural networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "152234f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "791b18458b7c49c887b53121bc7748a6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 10 \"observed\" data sets with different prior covariance (see above)\n",
    "observed_data = trainer.configurator(generative_model_obs(10))\n",
    "\n",
    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
    "    observed_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=False\n",
    ")\n",
    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0653338c-35c7-443c-b7e0-8ec3ed1d4575",
   "metadata": {},
   "source": [
    "We can also speed up the computations by using bootstraps of the reference data to generate the MMD distribution under the null hypothesis (`bootstrap=True`). This is particularly helpful if the simulator is computationally expensive and a large number of simulations is not computationally feasible.\n",
    "\n",
    "Here, we also provide the reference data ourselves, but bootstrapping can be performed either way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "eddfe447-4172-4755-9fdd-29d223ddbb17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c49cfaac20e44b68546b8f791852d36",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reference_data = trainer.configurator(trainer.generative_model(10))\n",
    "observed_data = trainer.configurator(generative_model_obs(10))\n",
    "\n",
    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
    "    observed_data, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
    ")\n",
    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "822fc97c-f4f4-49aa-8c2c-6d028b31ee9c",
   "metadata": {},
   "source": [
    "## Sensitivity to Misspecification\n",
    "\n",
    "The submodule `bayesflow.sensitivity` contains functions to analyze the sensitivity of a converged `Trainer` (i.e., the neural posterior estimator) to model misspecification."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53cf80ac",
   "metadata": {},
   "source": [
    "We start by redefining the generative model with the possibility to increase the model's misspecification through two settings `p1` and `p2`. Therefore, we define a function `generative_model_misspecified(p1, p2)`. The function takes the settings `p1` and `p2` as input and returns a (potentially misspecified) generative model.\n",
    "\n",
    "In our Gaussian example, we let `p1` control the prior location ($\\mu_0=\\mathtt{p1}$) while `p2` controls the scale of a diagonal covariance matrix $\\Sigma_0$ such that $\\Sigma_0=\\mathtt{p2}\\cdot\\mathbb{I}$. In this example, both settings cause prior misspecification. Inducing other types of misspecification (e.g., simulator or noise) follows the same principle.\n",
    "\n",
    "The consequence: If `p1=0` and `p2=1`, the `generative_model_misspecified` function yields the original well-specified model from training. For all other values for `p1` and `p2`, the resulting generative model differs.\n",
    "\n",
    "**Implementation details:** \n",
    "\n",
    "- The `partial` application pattern lets us pre-load the `prior` with custom arguments and pass this pre-loaded function into the generative model. We use this technique to use `p1` and `p2` as parameters in the prior callable.\n",
    "- We skip the generative model's consistency checks and setup outputs via `skip_test=True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "805ed033",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "\n",
    "\n",
    "def generative_model_misspecified(p1, p2):\n",
    "    prior_ = partial(prior, D=2, mu=p1, sigma=p2)\n",
    "    simulator_ = partial(simulator, scale=1.0)\n",
    "    generative_model_ = bf.simulation.GenerativeModel(prior_, simulator_, simulator_is_batched=False, skip_test=True)\n",
    "    return generative_model_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9afb3b0",
   "metadata": {},
   "source": [
    "In the next step, we provide meta-information for the sensitivity analysis:\n",
    "\n",
    "- Names of the settings `p1` and `p2`: proper axis labels\n",
    "- Range of the settings `p1` and `p2`: defining the experiment's grid\n",
    "- well-specified value for the settings `p1` and `p2` (i.e., `p1=0` and `p2=1` in our example): dashed lines for the baseline configuration in the plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "465f42d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "p1_config = {\n",
    "    \"name\": r\"$\\mu_0$ (prior location)\",\n",
    "    \"values\": np.linspace(-0.1, 3.1, num=20),\n",
    "    \"well_specified_value\": 0.0,\n",
    "}\n",
    "p2_config = {\n",
    "    \"name\": r\"$\\tau_0$ (prior scale)\",\n",
    "    \"values\": np.linspace(0.1, 10.1, num=20),\n",
    "    \"well_specified_value\": 1.0,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca8c198a",
   "metadata": {},
   "source": [
    "### Computing Sensitivity\n",
    "\n",
    "As described above, the `bf.sensitivity.misspecification_experiment` function requires the converged `Trainer`, the factory for misspecified models, and meta-information on the settings. In addition, the number of posterior samples per simulated data set as well as the total number of simulated data sets per setting configuration can be specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e0e87cd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [01:19<00:00,  3.99s/it]\n"
     ]
    }
   ],
   "source": [
    "posterior_error, summary_mmd = bf.sensitivity.misspecification_experiment(\n",
    "    trainer=trainer,\n",
    "    generator=generative_model_misspecified,\n",
    "    first_config_dict=p1_config,\n",
    "    second_config_dict=p2_config,\n",
    "    n_posterior_samples=500,\n",
    "    n_sim=100,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c919073b",
   "metadata": {},
   "source": [
    "### Plotting the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9317ad22",
   "metadata": {},
   "source": [
    "Model misspecification with respect to both prior location $\\mu_0$ and scale $\\tau_0$ worsen the average posterior recovery in terms of aggregated RMSE. However, the converged posterior approximator seems to be relatively robust against moderate misspecifications in these parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e1ab10c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = bf.sensitivity.plot_model_misspecification_sensitivity(\n",
    "    posterior_error, p1_config, p2_config, plot_config={\"vmin\": None}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee01b222",
   "metadata": {},
   "source": [
    "The MMD plot clearly shows that the summary space MMD is lowest when the model is well-specified (coordinates `(0, 1)`). When either the prior location $\\mu_0$ or the prior scale $\\tau_0$ changes, the summary MMD increases and we're alerted of the model misspecification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c05f8870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = bf.sensitivity.plot_model_misspecification_sensitivity(\n",
    "    summary_mmd, p1_config, p2_config, plot_config={\"vmin\": None}\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "165px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  },
  "toc-autonumbering": true,
  "toc-showtags": false
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
